diff --git a/DGE_README.md b/DGE_README.md new file mode 100644 index 000000000..5a9a89164 --- /dev/null +++ b/DGE_README.md @@ -0,0 +1,93 @@ +# MDV Differential Gene Expression (DGE) Implementation + +This document describes the "Tier 1" browser-based Differential Gene Expression (DGE) implementation in MDV. + +## 1. Overview + +The MDV DGE engine (`mdv-dge`) provides high-performance, real-time statistical analysis of single-cell RNA-seq data directly in the browser. It is designed to work with hundreds of thousands of cells and tens of thousands of genes without requiring a dedicated high-memory backend for computation. + +## 2. Architecture + +The DGE feature is split into four layers: + +1. **UI Layer (`DGEDialogReact.tsx`)**: A React/MUI dialog that allows users to select a grouping column (e.g., Leiden cluster, Disease state) and compare a target group against a reference group (or "rest"). +2. **Integration Layer (`dgeIntegration.ts`)**: The "glue" that connects MDV's `DataStore` and `ChartManager` to the DGE engine. It handles data discovery via `rows_as_columns` links, coordinates batch loading of gene data, and writes results back as new columns. +3. **Calculation Engine (`DGEDimension.ts` & `dgeWorker.ts`)**: Orchestrates the DGE pipeline. It divides genes into batches and can optionally offload calculations to **Web Workers** to keep the UI responsive. +4. **Statistical Core (`dgeStats.ts`)**: Pure TypeScript implementation of the statistical algorithms (Welch's t-test, Welford's online variance, Benjamini-Hochberg correction). + +## 3. Statistical Methodology + +### Welch's t-test +We use **Welch’s t-test** (unequal variances t-test) to compare gene expression between two groups. This is the same default method used by popular tools like **Scanpy**. + +* **P-value Calculation**: Computed using the Student's t-distribution CDF (via the Regularized Incomplete Beta function). +* **Multiple Testing Correction**: P-values are adjusted using the **Benjamini-Hochberg (BH)** procedure to control the False Discovery Rate (FDR). + +### Online Variance (Welford's Algorithm) +To handle large datasets without multiple passes through the data, we use **Welford’s online algorithm**. This allows us to calculate the mean and variance of gene expression in a single pass over the cell indices, minimizing memory pressure. + +### Adaptive Effect Size +The engine automatically detects the normalization of the input data and selects the appropriate effect size metric: + +* **Log1p-normalized data**: Uses **Log2 Fold Change (Log2FC)** with `expm1` back-transformation. +* **Linear / raw count data**: Uses **Log2 Fold Change (Log2FC)** directly on means (no `expm1`). +* **Z-scored data**: Uses **Mean Difference** (`meanTarget - meanReference`). + +Detection is automatic via a multi-gene probe (see gotcha below). + +### Gotcha: Data Type Detection for Sparse Raw Counts + +> **If the volcano plot shows many genes clamped at exactly ±10 effect size, or highly-expressed housekeeping genes have implausible log2FC values, check that data type detection is working correctly.** + +The engine probes the first 50 genes to decide if expression values are log1p-normalized (small non-negative, max ≤ 20), linear/raw counts (non-negative, max > 20), or z-scored (has negatives). For **sparse raw count data**, most genes have very low counts (1–3 per cell) which look identical to log1p-normalized values. If detection breaks at the first gene it finds, it may misclassify raw counts as log1p — causing `expm1()` to be applied to already-linear values, which exponentially inflates fold changes for highly-expressed genes. + +**The fix** (implemented in `dgeIntegration.ts`): the probe loop scans across **all** 50 sampled genes, tracking the global maximum value, and only decides after examining all of them. It exits early only when it finds a definitive signal (a negative value → z-scored, or a value > 20 → linear). + +Even with correct detection, genes expressed exclusively in one group (e.g., 72 UC cells, 0 Healthy cells) will produce mathematically extreme fold changes that are clamped to ±10. This is expected for very sparse genes and can be mitigated with a minimum cell count filter if needed. + +## 4. Performance & Scalability + +### Batching +Data is requested from the backend in batches (default: **2000 genes per batch**). This strikes a balance between network overhead and browser memory usage. + +### Memory Optimization +* **SharedArrayBuffer**: Expression data is loaded into `SharedArrayBuffer` objects, allowing efficient sharing between the main thread and Web Workers. +* **Sparse Data Handling**: In sparse datasets, `NaN` values in MDV's dense buffers are treated as `0` during statistical accumulation, ensuring correct cell counts for variance calculations. + +### Browser Limits +* **Memory**: Each gene for 60k cells takes ~240KB. A batch of 2000 genes takes ~480MB. +* **Dataset Size**: While Tier 1 works well for up to 500k cells, datasets of **10M+ cells** require either sub-sampling or server-side execution (Tier 2) due to browser memory and transfer limits. + + ## 5. Backend Integration: HTTP Streaming + + To prevent the backend from crashing when requesting thousands of columns at once, we implemented **HTTP Streaming** (Chunked Transfer Encoding) in the Flask server: + + * **`yield_byte_data`**: The Python backend streams individual gene columns from HDF5 as they are read, instead of buffering the entire multi-gigabyte response in RAM. + * **No Content-Length**: This removes the risk of `ERR_CONTENT_LENGTH_MISMATCH` errors caused by large binary payloads. + +## 6. Configuration & Troubleshooting + +### Adjusting Batch Size +If the UI feels sluggish or you encounter memory errors, you can adjust the default DGE batch size in the following centralized location: + +* **File:** `src/lib/constants.ts` +* **Constant:** `DEFAULT_DGE_BATCH_SIZE` + +This single source of truth is used as the default fallback in both the integration layer (`dgeIntegration.ts`) and the core DGE engine (`DGEDimension.ts`). + +**After making changes** you must perform a hard browser refresh (Cmd+Shift+R or Ctrl+F5) to clear the cached JavaScript. + +#### Guidance on choosing a value + +| Batch Size | Effect | +| :--- | :--- | +| **500** | Safer for large datasets (>100k cells). Lower peak memory per batch (~120MB). More HTTP round-trips. | +| **1000** | Good balance for datasets up to ~200k cells. | +| **2000** *(default)* | Optimal for datasets up to ~60k cells. Reduces round-trips significantly. | +| **5000+** | Only suitable for small datasets (<20k cells). Risk of browser memory pressure / GC pauses. | + +### Visualizing Results +After a DGE run completes, a **Volcano Plot** is automatically generated. You can also manually create scatter plots or color other charts using the newly generated `dge_effect_size` and `dge_neg_log10_pval_adj` columns. + +### Caching +DGE results are cached in memory. If you run DGE a second time on the same dataset, the browser will reuse the cached gene expression data, making the second run nearly instantaneous. diff --git a/python/mdvtools/mdvproject.py b/python/mdvtools/mdvproject.py index f5a8cbf18..2112648b7 100644 --- a/python/mdvtools/mdvproject.py +++ b/python/mdvtools/mdvproject.py @@ -1755,6 +1755,7 @@ def get_byte_data(self, columns, group): byte_list = [] gr = h5[group] if not isinstance(gr, h5py.Group): + h5.close() raise TypeError("Expected 'gr' to be of type h5py.Group.") for column in columns: sg = column.get("subgroup") @@ -1771,6 +1772,27 @@ def get_byte_data(self, columns, group): h5.close() return b"".join(byte_list) + def yield_byte_data(self, columns, group): + """ + Generator version of get_byte_data for HTTP streaming. + Yields bytes for each column one by one to avoid large memory allocations + and Content-Length mismatches. + """ + with h5py.File(self.h5file, "r") as h5: + gr = h5[group] + if not isinstance(gr, h5py.Group): + raise TypeError("Expected 'gr' to be of type h5py.Group.") + for column in columns: + sg = column.get("subgroup") + if sg: + sgindex = int(column["sgindex"]) + yield get_subgroup_bytes( + gr[sg], sgindex, column.get("sgtype") == "sparse" + ) + else: + data = gr[column["field"]] + yield numpy.array(data).tobytes() + def set_region_data( self, datasource, diff --git a/python/mdvtools/server.py b/python/mdvtools/server.py index 150fd2f2f..7d2cf22ac 100644 --- a/python/mdvtools/server.py +++ b/python/mdvtools/server.py @@ -5,7 +5,9 @@ request, make_response, jsonify, - current_app + current_app, + Response, + stream_with_context ) from mdvtools.server_utils import ( send_file, @@ -196,10 +198,12 @@ def get_data(): raise Exception( "Request must contain JSON with 'columns' and 'data_source'" ) - bytes_ = project.get_byte_data(data["columns"], data["data_source"]) - response = make_response(bytes_) - response.headers.set("Content-Type", "application/octet-stream") - return response + + # Use streaming to avoid large memory allocations and Content-Length mismatches + return Response( + stream_with_context(project.yield_byte_data(data["columns"], data["data_source"])), + mimetype="application/octet-stream" + ) except Exception as e: log(e) return "Problem handling request", 400 diff --git a/python/mdvtools/server_utils.py b/python/mdvtools/server_utils.py index 2f10ae509..3caf264be 100644 --- a/python/mdvtools/server_utils.py +++ b/python/mdvtools/server_utils.py @@ -26,7 +26,8 @@ def add_safe_headers(resp): resp.headers["Cross-Origin-Embedder-Policy"] = "require-corp" # headers required if serving endpoints for another server e,g dev server resp.headers["Access-Control-Allow-Origin"] = "*" - resp.headers["Access-Control-Allow-Headers"] = "Content-Type" + # Allow custom debug/request correlation headers used by the frontend. + resp.headers["Access-Control-Allow-Headers"] = "Content-Type, X-MDV-Req-Id" #required for vite dev resp.headers["Cross-Origin-Resource-Policy"] ="cross-origin" return resp diff --git a/python/mdvtools/tests/generate_dge_reference.py b/python/mdvtools/tests/generate_dge_reference.py new file mode 100644 index 000000000..8de511f8b --- /dev/null +++ b/python/mdvtools/tests/generate_dge_reference.py @@ -0,0 +1,142 @@ +"""Generate Scanpy DGE reference data for mdv-dge validation. + +Runs rank_genes_groups on the PBMC3k dataset for all leiden clusters vs rest, +exports results as JSON fixtures for use in vitest browser-side DGE tests. + +Usage: + cd /app/python && poetry run python -m mdvtools.tests.generate_dge_reference +""" + +import json +import sys +import numpy as np +import scanpy as sc + + +H5AD_PATH = "/app/mdv/16/scanpy-pbmc3k.h5ad" +FIXTURE_DIR = "/app/src/tests/fixtures" +TOP_N_SMALL = 50 +VALIDATION_GENES = 100 + + +def extract_results(adata, method: str) -> dict: + """Extract rank_genes_groups results into a serializable dict.""" + result = sc.get.rank_genes_groups_df(adata, group=None) + + groups = {} + for group_name in result["group"].unique(): + gdf = result[result["group"] == group_name].copy() + gdf = gdf.sort_values("pvals") + groups[str(group_name)] = { + "genes": gdf["names"].tolist(), + "scores": [float(x) for x in gdf["scores"]], + "log2fc": [float(x) for x in gdf["logfoldchanges"]], + "pvals": [float(x) for x in gdf["pvals"]], + "pvals_adj": [float(x) for x in gdf["pvals_adj"]], + } + + group_sizes = adata.obs["leiden"].value_counts().to_dict() + + return { + "method": method, + "groupby": "leiden", + "reference": "rest", + "n_cells": int(adata.n_obs), + "n_genes": int(adata.n_vars), + "group_sizes": {str(k): int(v) for k, v in group_sizes.items()}, + "groups": groups, + } + + +def extract_small(full_results: dict) -> dict: + """Extract top N genes per group for fast unit tests.""" + small = {k: v for k, v in full_results.items() if k != "groups"} + small["top_n"] = TOP_N_SMALL + small["groups"] = {} + for gname, gdata in full_results["groups"].items(): + small["groups"][gname] = { + k: v[:TOP_N_SMALL] for k, v in gdata.items() + } + return small + + +def extract_validation_data(adata) -> dict: + """Extract log1p-normalized expression data for a subset of genes + leiden assignments. + + Uses adata.raw.X (log1p-normalized) since that's what rank_genes_groups uses + by default. This allows the JS test to recompute DGE from scratch and compare. + """ + # Use raw data (log1p-normalized) to match rank_genes_groups default behavior + raw = adata.raw + raw_gene_names = raw.var_names.tolist() + + # Select genes evenly spaced from the raw gene set, but only those that + # appear in the rank_genes_groups results (i.e. all raw genes) + selected_indices = np.linspace(0, len(raw_gene_names) - 1, VALIDATION_GENES, dtype=int) + selected_genes = [raw_gene_names[i] for i in selected_indices] + + X = raw.X + if hasattr(X, "toarray"): + X = X.toarray() + expression = {} + for idx, gene in zip(selected_indices, selected_genes): + expression[gene] = [float(x) for x in X[:, idx]] + + leiden = adata.obs["leiden"].tolist() + + return { + "n_cells": int(adata.n_obs), + "n_genes_total": int(raw.n_vars), + "n_genes_selected": len(selected_genes), + "genes": selected_genes, + "leiden": leiden, + "expression": expression, + } + + +def write_json(data: dict, path: str): + with open(path, "w") as f: + json.dump(data, f, allow_nan=False, separators=(",", ":")) + size_mb = len(json.dumps(data, separators=(",", ":"))) / 1024 / 1024 + print(f" Written {path} ({size_mb:.1f} MB)") + + +def main(): + print(f"Loading {H5AD_PATH}...") + adata = sc.read_h5ad(H5AD_PATH) + print(f" {adata.n_obs} cells, {adata.n_vars} genes") + print(f" Leiden clusters: {sorted(adata.obs['leiden'].unique())}") + + # --- T-test --- + print("\nRunning rank_genes_groups (t-test)...") + sc.tl.rank_genes_groups(adata, "leiden", method="t-test", reference="rest") + ttest_full = extract_results(adata, "t-test") + ttest_small = extract_small(ttest_full) + + write_json(ttest_full, f"{FIXTURE_DIR}/dge_reference_ttest.json") + write_json(ttest_small, f"{FIXTURE_DIR}/dge_reference_ttest_small.json") + + # --- Wilcoxon --- + print("\nRunning rank_genes_groups (wilcoxon)...") + sc.tl.rank_genes_groups(adata, "leiden", method="wilcoxon", reference="rest") + wilcoxon_full = extract_results(adata, "wilcoxon") + wilcoxon_small = extract_small(wilcoxon_full) + + write_json(wilcoxon_full, f"{FIXTURE_DIR}/dge_reference_wilcoxon.json") + write_json(wilcoxon_small, f"{FIXTURE_DIR}/dge_reference_wilcoxon_small.json") + + # --- Validation expression data --- + print("\nExtracting validation expression data...") + validation = extract_validation_data(adata) + write_json(validation, f"{FIXTURE_DIR}/dge_validation_data.json") + + print("\nDone. Generated fixtures:") + print(f" {FIXTURE_DIR}/dge_reference_ttest.json") + print(f" {FIXTURE_DIR}/dge_reference_ttest_small.json") + print(f" {FIXTURE_DIR}/dge_reference_wilcoxon.json") + print(f" {FIXTURE_DIR}/dge_reference_wilcoxon_small.json") + print(f" {FIXTURE_DIR}/dge_validation_data.json") + + +if __name__ == "__main__": + main() diff --git a/src/charts/ChartManager.js b/src/charts/ChartManager.js index 58ba49084..8f2b98785 100644 --- a/src/charts/ChartManager.js +++ b/src/charts/ChartManager.js @@ -63,6 +63,7 @@ import ErrorComponentReactWrapper from "@/react/components/ErrorComponentReactWr import ViewDialogWrapper from "./dialogs/ViewDialogWrapper"; import { deserialiseParam, getConcreteFieldNames } from "./chartConfigUtils"; import AddChartDialogReact from "./dialogs/AddChartDialogReact"; +import DGEDialogReact from "./dialogs/DGEDialogReact"; import MenuBarWrapper from "@/react/components/MenuBarComponent"; @@ -1582,6 +1583,17 @@ export class ChartManager { new BaseDialog.experiment["AnnotationDialogReact"](ds.dataStore); }); + if (dataStore.links) { + const hasRac = Object.values(dataStore.links).some( + (linkSet) => linkSet.rows_as_columns, + ); + if (hasRac) { + this.addMenuIcon(ds.name, "fas fa-dna", "Run DGE Analysis", () => { + new DGEDialogReact(dataStore); + }); + } + } + const idiv = createEl( "div", { @@ -1921,7 +1933,7 @@ export class ChartManager { } for (const c of colInfo) { - ds.setColumnData(c.col, c.data); + ds.setColumnData(c.col, c.data, { recomputeStats: false }); } func(); }); diff --git a/src/charts/dialogs/DGEDialogReact.tsx b/src/charts/dialogs/DGEDialogReact.tsx new file mode 100644 index 000000000..6b4791338 --- /dev/null +++ b/src/charts/dialogs/DGEDialogReact.tsx @@ -0,0 +1,415 @@ +import { useCallback, useEffect, useMemo, useRef, useState } from "react"; +import { + Box, + Button, + FormControl, + InputLabel, + LinearProgress, + MenuItem, + Select, + Typography, +} from "@mui/material"; +import type DataStore from "../../datastore/DataStore.js"; +import { BaseDialog } from "../../utilities/Dialog.js"; +import { createMdvPortal } from "@/react/react_utils"; +import { + runDGEOnDataStore, + findDGECapableDatasources, +} from "../../datastore/dgeIntegration"; +import type { DGEIntegrationResult } from "../../datastore/dgeIntegration"; +import { getRandomString } from "../../utilities/Utilities"; +import { useChartManager } from "@/react/hooks"; +import type { DGEChartManager } from "../../datastore/dgeIntegration"; +import { DEFAULT_DGE_BATCH_SIZE } from "../../lib/constants"; + +interface DGEDialogContentProps { + dataStore: DataStore; + onClose: () => void; +} + +function DGEDialogContent({ dataStore, onClose }: DGEDialogContentProps) { + const chartManager = useChartManager() as unknown as DGEChartManager; + const [groupColumn, setGroupColumn] = useState(""); + const [targetGroup, setTargetGroup] = useState(""); + const [referenceGroup, setReferenceGroup] = useState("rest"); + const [selectedGenesDsName, setSelectedGenesDsName] = useState(""); + const [running, setRunning] = useState(false); + const [progress, setProgress] = useState({ done: 0, total: 0 }); + const [result, setResult] = useState(null); + const [error, setError] = useState(null); + const isMountedRef = useRef(true); + const runAbortRef = useRef(null); + + useEffect(() => { + // Keep this resilient in React StrictMode (dev), where mount effects + // are intentionally mounted/cleaned up/re-mounted. + isMountedRef.current = true; + return () => { + isMountedRef.current = false; + runAbortRef.current?.abort(); + runAbortRef.current = null; + }; + }, []); + + const categoricalColumns = useMemo(() => { + return dataStore.columns.filter( + (c: any) => + (c.datatype === "text" || c.datatype === "text16") && c.values, + ); + }, [dataStore]); + + const selectedColumnValues = useMemo(() => { + if (!groupColumn) return []; + const col = dataStore.columnIndex[groupColumn]; + return col?.values ?? []; + }, [dataStore, groupColumn]); + + const safeTargetGroup = selectedColumnValues.includes(targetGroup) ? targetGroup : ""; + const safeReferenceGroup = referenceGroup === "rest" || selectedColumnValues.includes(referenceGroup) + ? referenceGroup : "rest"; + + const genesDatasourceOptions = useMemo(() => { + return findDGECapableDatasources(chartManager) + .filter((p) => p.cellsDsName === dataStore.name) + .map((p) => p.genesDsName) + .sort((a, b) => a.localeCompare(b)); + }, [chartManager, dataStore.name]); + + const referenceOptions = useMemo(() => { + return ["rest", ...selectedColumnValues.filter((v: string) => v !== safeTargetGroup)]; + }, [selectedColumnValues, safeTargetGroup]); + + useEffect(() => { + if (categoricalColumns.length > 0 && !groupColumn) { + setGroupColumn(categoricalColumns[0].field); + } + }, [categoricalColumns, groupColumn]); + + useEffect(() => { + if (selectedColumnValues.length > 0) { + setTargetGroup(selectedColumnValues[0]); + setReferenceGroup("rest"); + } + }, [selectedColumnValues]); + + useEffect(() => { + if (genesDatasourceOptions.length === 0) { + setSelectedGenesDsName(""); + return; + } + if (!genesDatasourceOptions.includes(selectedGenesDsName)) { + setSelectedGenesDsName(genesDatasourceOptions[0]); + } + }, [genesDatasourceOptions, selectedGenesDsName]); + + const handleAbort = useCallback(() => { + const currentRun = runAbortRef.current; + if (!currentRun) return; + currentRun.abort(); + runAbortRef.current = null; + if (!isMountedRef.current) return; + setRunning(false); + setProgress({ done: 0, total: 0 }); + setError("DGE run aborted."); + }, []); + + const handleRun = useCallback(async () => { + const w: any = window as any; + const getBrowserMemoryBudgetBytes = () => { + const jsHeapLimit = w?.performance?.memory?.jsHeapSizeLimit; + const deviceMemGB = w?.navigator?.deviceMemory; + const candidates: number[] = []; + if (typeof jsHeapLimit === "number" && Number.isFinite(jsHeapLimit) && jsHeapLimit > 0) { + candidates.push(jsHeapLimit * 0.6); + } + if (typeof deviceMemGB === "number" && Number.isFinite(deviceMemGB) && deviceMemGB > 0) { + candidates.push(deviceMemGB * 1024 * 1024 * 1024 * 0.25); + } + if (!candidates.length) return null; + return Math.min(...candidates); + }; + + const cellsCount = dataStore.size ?? 0; + const genesCount = selectedGenesDsName + ? chartManager?.dsIndex?.[selectedGenesDsName]?.dataStore?.size ?? null + : null; + const budgetBytes = getBrowserMemoryBudgetBytes(); + // Better estimate: DGE loads gene expression columns in batches. + // Each gene expression column is a Float32Array over all cells (~4 bytes per cell). + const batchSize = DEFAULT_DGE_BATCH_SIZE; + const bytesPerGeneColumn = cellsCount * 4; + const bytesPerBatch = bytesPerGeneColumn * batchSize; + const probeColumns = 50; // from runDGEOnDataStore MAX_PROBE + const bytesForProbe = bytesPerGeneColumn * Math.min(probeColumns, genesCount ?? probeColumns); + // Shared buffers always present: + const bytesForFilter = cellsCount; // Uint8 filterBuffer + const bytesForGroup = cellsCount; // Uint8 groupSAB + // DGE result columns written to genes DS (6 Float32 arrays sized by gene count). + const bytesForResults = (genesCount ?? 0) * 4 * 6; + // Peak-ish estimate: one batch of gene columns + existing buffers + results + some overhead factor. + const estimatedMinBytes = bytesPerBatch + bytesForProbe + bytesForFilter + bytesForGroup + bytesForResults; + const headroomFactor = 2.0; + const budgetUnknown = budgetBytes == null; + const shouldWarn = + // If we have a budget, compare estimate to it. + (!budgetUnknown && estimatedMinBytes * headroomFactor > (budgetBytes as number)) || + // If we *don't* have a budget signal, only warn for clearly-large runs. + (budgetUnknown && (cellsCount >= 500_000 || (genesCount ?? 0) >= 50_000)); + + if (shouldWarn) { + const estimatedMB = estimatedMinBytes / (1024 * 1024); + const budgetMB = budgetBytes == null ? null : budgetBytes / (1024 * 1024); + const msg = + `This DGE run will process ${cellsCount.toLocaleString()} cells` + + (genesCount ? ` and ${genesCount.toLocaleString()} genes` : "") + + ". This is likely to consume a lot of browser memory and may crash (OOM / Array buffer allocation failed)." + + `\n\nEstimate (very rough): ~${estimatedMB.toFixed(0)} MB minimum` + + (budgetMB == null ? "" : `; budget ~${budgetMB.toFixed(0)} MB`) + + ".\n\nContinue?"; + + if (!window.confirm(msg)) { + return; + } + } + + runAbortRef.current?.abort(); + const abortController = new AbortController(); + runAbortRef.current = abortController; + + setRunning(true); + setError(null); + setResult(null); + setProgress({ done: 0, total: 0 }); + + console.log("=== DGE RUN START ==="); + console.log("groupColumn:", groupColumn, "target:", safeTargetGroup, "reference:", safeReferenceGroup); + console.log("dataStore:", dataStore.name, "size:", dataStore.size); + + try { + const cm = chartManager; + if (!selectedGenesDsName) { + throw new Error("Select a linked genes datasource"); + } + + const dgeResult = await runDGEOnDataStore( + cm, + dataStore.name, + selectedGenesDsName, + { + groupColumn, + targetGroup: safeTargetGroup, + referenceGroup: safeReferenceGroup, + }, + (done, total) => { + if (isMountedRef.current && !abortController.signal.aborted) { + setProgress({ done, total }); + } + }, + { signal: abortController.signal }, + ); + if (!isMountedRef.current || abortController.signal.aborted) return; + + setResult(dgeResult); + + // Verify DGE columns exist on genes DS before charting + const gDS = cm.dsIndex[selectedGenesDsName]?.dataStore; + if (gDS) { + for (const f of ["dge_effect_size", "dge_neg_log10_pval_adj"]) { + const c = gDS.columnIndex[f]; + if (!c) { console.error("[DGE] column missing:", f); continue; } + const d = c.data; + if (!d) { console.error("[DGE] column has no data:", f); continue; } + let valid = 0, nanCount = 0, min = Infinity, max = -Infinity; + for (let i = 0; i < d.length; i++) { + if (Number.isNaN(d[i])) { nanCount++; } else { valid++; if (d[i] < min) min = d[i]; if (d[i] > max) max = d[i]; } + } + console.log(`[DGE] genes DS col "${f}": length=${d.length}, valid=${valid}, NaN=${nanCount}, min=${min}, max=${max}, minMax=`, c.minMax); + } + console.log("[DGE] genes DS size:", gDS.size, "columnsWithData:", gDS.columnsWithData.filter((x: string) => x.startsWith("dge_"))); + } + + const volcanoChartId = `dge_volcano_${getRandomString()}`; + try { + await cm.addChart(selectedGenesDsName, { + id: volcanoChartId, + type: "wgl_scatter_plot", + title: `DGE: ${safeTargetGroup} vs ${safeReferenceGroup}`, + param: ["dge_effect_size", "dge_neg_log10_pval_adj"], + size: [500, 400], + }, true); + } catch (err: any) { + if (abortController.signal.aborted) return; + console.warn("DGE completed but opening volcano plot failed", { + error: err, + volcanoChartId, + selectedGenesDsName, + safeTargetGroup, + safeReferenceGroup, + }); + } + } catch (e: any) { + if (abortController.signal.aborted || !isMountedRef.current) return; + if (e?.name !== "AbortError") { + setError(e.message || String(e)); + } + } finally { + const isCurrentRun = runAbortRef.current === abortController; + if (isCurrentRun) { + runAbortRef.current = null; + } + // Always settle "running" for the currently tracked run, including aborts. + if (isMountedRef.current && isCurrentRun) { + setRunning(false); + } + } + }, [chartManager, dataStore.name, dataStore.size, groupColumn, safeTargetGroup, safeReferenceGroup, selectedGenesDsName]); + + const sigCount = result + ? result.results.filter((r) => r.pvalAdj < 0.05).length + : 0; + + return ( + + + DGE Datasource + + + + + Grouping Column + + + + + Target Group + + + + + Reference + + + + + + {running && ( + + 0 ? "determinate" : "indeterminate"} + value={progress.total > 0 ? (progress.done / progress.total) * 100 : 0} + /> + + {progress.total > 0 + ? `Loading gene columns... (batch ${progress.done}/${progress.total})` + : "Initializing..."} + + + + )} + + {error && ( + + {error} + + )} + + {result && ( + + + Found {sigCount} significant genes (padj < 0.05) in{" "} + {(result.elapsed / 1000).toFixed(1)}s + + + Effect size: {result.effectSizeLabel === "log2fc" ? "log2 fold change" : "mean difference"} + + {result.skippedGenes > 0 && ( + + {result.skippedGenes} genes skipped (failed to load) + + )} + + )} + + ); +} + +export default class DGEDialogReact extends BaseDialog { + root: ReturnType; + constructor(dataStore: DataStore) { + super( + { + title: "Differential Gene Expression", + width: 400, + height: 480, + }, + null, + ); + this.root = createMdvPortal( + this.close()} />, + this.dialog, + this, + ); + } + close(): void { + super.close(); + this.root.unmount(); + } +} diff --git a/src/dataloaders/DataLoaders.ts b/src/dataloaders/DataLoaders.ts index 3ce850134..960fea63e 100644 --- a/src/dataloaders/DataLoaders.ts +++ b/src/dataloaders/DataLoaders.ts @@ -17,6 +17,11 @@ import type { TypedArrayConstructor } from "@luma.gl/core"; type Columns = DataColumn[]; +let _mdvGetDataInFlight = 0; +let _mdvGetDataSerialize: Promise = Promise.resolve(); + +// (debug-only helpers removed) + /** * @memberof module:DataLoaders * @param data - an array buffer containing raw concatenated @@ -126,18 +131,46 @@ function getArrayBufferDataLoader(url: string, decompress = false) { dataSource: DataSourceName, size: number, ) => { - //get the data - const response = await fetch(url, { - method: "POST", - body: JSON.stringify({ columns: columns, data_source: dataSource }), - headers: { - "Content-Type": "application/json", - }, - }); - //the data is any arraybuffer containing each individual - //column's raw data - let data: ArrayBufferLike = await response.arrayBuffer(); + _mdvGetDataInFlight += 1; + const shouldSerialize = size >= 500_000; + + let releaseSerialize: (() => void) | null = null; + if (shouldSerialize) { + const prev = _mdvGetDataSerialize; + _mdvGetDataSerialize = new Promise((resolve) => { + releaseSerialize = resolve; + }); + await prev; + } + const fetchUrl = url; + + let response: Response; + try { + response = await fetch(fetchUrl, { + method: "POST", + body: JSON.stringify({ columns: columns, data_source: dataSource }), + headers: { + "Content-Type": "application/json", + }, + }); + } catch (e: any) { + _mdvGetDataInFlight -= 1; + if (releaseSerialize) releaseSerialize(); + throw e; + } + + let data: ArrayBufferLike; + try { + data = await response.arrayBuffer(); + } catch (e: any) { + _mdvGetDataInFlight -= 1; + if (releaseSerialize) releaseSerialize(); + throw e; + } + data = decompress ? await decompressData(new Uint8Array(data)) : data; + _mdvGetDataInFlight -= 1; + if (releaseSerialize) releaseSerialize(); return processArrayBuffer(data, columns, size); }; } diff --git a/src/datastore/DGEDimension.ts b/src/datastore/DGEDimension.ts new file mode 100644 index 000000000..3538bf5de --- /dev/null +++ b/src/datastore/DGEDimension.ts @@ -0,0 +1,228 @@ +/** + * mdv-dge: Orchestrator that bridges MDV's DataStore column-loading + * infrastructure with the DGE Web Worker. + * + * Loads gene expression columns in batches, posts them to dgeWorker, + * collects results, applies BH correction, and emits the final table. + */ + +import { benjaminiHochberg } from "./dgeStats"; +import type { GeneResult } from "./dgeStats"; +import type { DGEBatchInput, DGEBatchOutput } from "./dgeWorker"; +import { processBatch } from "./dgeWorker"; +import { DEFAULT_DGE_BATCH_SIZE } from "../lib/constants"; + +export interface DGEConfig { + groupColumn: string; + targetGroup: string; + referenceGroup: string | "rest"; + batchSize?: number; +} + +export interface DGEFullResult extends GeneResult { + pvalAdj: number; + negLog10Pval: number; + negLog10PvalAdj: number; +} + +export type EffectSizeLabel = "log2fc" | "mean_diff"; + +export interface DGERunResult { + config: DGEConfig; + results: DGEFullResult[]; + elapsed: number; + effectSizeLabel: EffectSizeLabel; + skippedGenes: number; +} + +type ColumnLoader = (columns: string[]) => Promise; +type GetColumnBuffer = (field: string) => SharedArrayBuffer | null; +type AbortLike = AbortSignal | undefined; + +/** + * Standalone DGE runner that works with any DataStore-like data source. + * + * Rather than extending Dimension (which is for filtering), this is a + * utility class that orchestrates the full DGE pipeline: + * 1. Resolve gene column field names from the rows_as_columns link + * 2. Load gene columns in batches via the provided loader + * 3. Run DGE computation (in-thread or via Worker) + * 4. Apply BH correction + * 5. Return the full results table + */ +export class DGERunner { + private worker: Worker | null = null; + + /** + * Run DGE analysis. + * + * @param config DGE configuration (group column, target, reference) + * @param filterBuffer DataStore.filterBuffer (SharedArrayBuffer) + * @param size Number of cells (DataStore.size) + * @param groupBuffer SharedArrayBuffer for the group column (Uint8Array-typed) + * @param groupValues Category labels for the group column + * @param geneFields Array of gene column field names (gs|NAME (scores)|INDEX format) + * @param geneNames Array of human-readable gene names (parallel to geneFields) + * @param loadColumns Async function that loads columns into the DataStore + * @param getColumnBuffer Function that returns the SharedArrayBuffer for a loaded column + * @param onProgress Optional progress callback (batchesDone, totalBatches) + * @param useWorker Whether to use a Web Worker (true) or compute in-thread (false) + * @param dataType Type of data (log1p, linear, or zscored) + */ + async run( + config: DGEConfig, + filterBuffer: SharedArrayBuffer, + size: number, + groupBuffer: SharedArrayBuffer, + groupValues: string[], + geneFields: string[], + geneNames: string[], + loadColumns: ColumnLoader, + getColumnBuffer: GetColumnBuffer, + onProgress?: (done: number, total: number) => void, + useWorker = false, + dataType: "log1p" | "linear" | "zscored" = "log1p", + signal?: AbortLike, + ): Promise { + const throwIfAborted = () => { + if (signal?.aborted) { + throw new DOMException("DGE run aborted", "AbortError"); + } + }; + + const t0 = performance.now(); + const batchSize = config.batchSize ?? DEFAULT_DGE_BATCH_SIZE; + throwIfAborted(); + + const targetIdx = groupValues.indexOf(config.targetGroup); + if (targetIdx < 0) { + throw new Error(`Target group "${config.targetGroup}" not found in group values: ${groupValues.join(", ")}`); + } + let referenceIdx = -1; + if (config.referenceGroup !== "rest") { + referenceIdx = groupValues.indexOf(config.referenceGroup); + if (referenceIdx < 0) { + throw new Error(`Reference group "${config.referenceGroup}" not found in group values: ${groupValues.join(", ")}`); + } + } + + const allBatchResults: GeneResult[] = []; + const totalBatches = Math.ceil(geneFields.length / batchSize); + let skippedGenes = 0; + + for (let b = 0; b < totalBatches; b++) { + throwIfAborted(); + const start = b * batchSize; + const end = Math.min(start + batchSize, geneFields.length); + const batchFields = geneFields.slice(start, end); + const batchNames = geneNames.slice(start, end); + + await loadColumns(batchFields); + throwIfAborted(); + + const loadedNames: string[] = []; + const geneBuffers: SharedArrayBuffer[] = []; + for (let i = 0; i < batchFields.length; i++) { + const buf = getColumnBuffer(batchFields[i]); + if (!buf) { + skippedGenes++; + continue; + } + loadedNames.push(batchNames[i]); + geneBuffers.push(buf); + } + + if (geneBuffers.length === 0) { + onProgress?.(b + 1, totalBatches); + continue; + } + + const input: DGEBatchInput = { + type: "run_batch", + filterBuffer, + groupBuffer, + targetGroup: targetIdx, + referenceGroup: referenceIdx, + geneBuffers, + geneNames: loadedNames, + batchIndex: b, + dataType, + }; + + let batchResult: DGEBatchOutput; + if (useWorker) { + batchResult = await this.runInWorker(input); + } else { + batchResult = processBatch(input); + } + throwIfAborted(); + + allBatchResults.push(...batchResult.results); + + if (b === 0) { + const top5 = [...batchResult.results] + .filter(r => !Number.isNaN(r.pval)) + .sort((a, b) => a.pval - b.pval) + .slice(0, 5); + console.log(`[DGE diag] Batch 0 results (${batchResult.results.length} genes, ${loadedNames.length} loaded):`); + for (const r of top5) { + console.log(`[DGE diag] ${r.gene}: effectSize=${r.effectSize.toFixed(4)}, pval=${r.pval.toExponential(3)}, meanTarget=${r.meanTarget.toFixed(4)}, meanRef=${r.meanReference.toFixed(4)}`); + } + const allPvals = batchResult.results.map(r => r.pval).filter(p => !Number.isNaN(p)); + const sigCount = allPvals.filter(p => p < 0.05).length; + console.log(`[DGE diag] Batch 0 sig (p<0.05): ${sigCount}/${allPvals.length}, NaN p-values: ${batchResult.results.length - allPvals.length}`); + } + + onProgress?.(b + 1, totalBatches); + } + + if (skippedGenes > 0) { + console.warn(`DGE: skipped ${skippedGenes} genes due to failed column loads`); + } + + const pvals = allBatchResults.map((r) => r.pval); + const adjustedPvals = benjaminiHochberg(pvals); + + const results: DGEFullResult[] = allBatchResults.map((r, i) => ({ + ...r, + pvalAdj: adjustedPvals[i], + negLog10Pval: r.pval > 0 ? -Math.log10(r.pval) : 300, + negLog10PvalAdj: adjustedPvals[i] > 0 ? -Math.log10(adjustedPvals[i]) : 300, + })); + + results.sort((a, b) => a.pval - b.pval); + + return { + config, + results, + elapsed: performance.now() - t0, + effectSizeLabel: dataType === "log1p" || dataType === "linear" ? "log2fc" : "mean_diff", + skippedGenes, + }; + } + + private runInWorker(input: DGEBatchInput): Promise { + if (!this.worker) { + this.worker = new Worker( + new URL("./dgeWorker.ts", import.meta.url), + ); + } + const w = this.worker; + return new Promise((resolve, reject) => { + w.onmessage = (e: MessageEvent) => { + resolve(e.data); + }; + w.onerror = (e) => { + reject(new Error(`DGE Worker error: ${e.message}`)); + }; + w.postMessage(input); + }); + } + + destroy(): void { + if (this.worker) { + this.worker.terminate(); + this.worker = null; + } + } +} diff --git a/src/datastore/DataStore.js b/src/datastore/DataStore.js index 651405cb7..3a1e598f5 100644 --- a/src/datastore/DataStore.js +++ b/src/datastore/DataStore.js @@ -517,7 +517,8 @@ class DataStore { this.columns.push(c); this.columnIndex[c.field] = c; if (data) { - this.setColumnData(column.field, data); + // Preserve pre-specified stats (e.g. minMax) when provided via metadata. + this.setColumnData(column.field, data, { recomputeStats: false }); } if (dirty) { this.dirtyColumns.added[column.field] = true; @@ -1195,13 +1196,16 @@ class DataStore { * format see [columns](../../docs/extradocs/datasource.md) * @param {string} column - The field/id of the column. * @param {SharedArrayBuffer|Array} data either a javascript array or shared array buffer + * @param {{recomputeStats?: boolean}?} opts - options for updating derived stats */ - setColumnData(column, data) { + setColumnData(column, data, opts = null) { // if (Math.random() < 0.1) throw new Error("This is a test error"); const c = this.columnIndex[column]; if (!c) { throw `column ${column} is not present in data store`; } + const recomputeStats = + opts == null || opts.recomputeStats == null ? true : !!opts.recomputeStats; let buffer = null; if (Array.isArray(data)) { buffer = this._convertColumn(c, data); @@ -1218,6 +1222,10 @@ class DataStore { c.datatype === "int32" ? new Int32Array(buffer) : new Float32Array(buffer)); + if (recomputeStats) { + c.minMax = null; + c.quantiles = null; + } if (!c.minMax) { console.log(`Calculating min/max for ${c.name}`); const computed = this._computeMinMaxFromData(c); diff --git a/src/datastore/dgeIntegration.ts b/src/datastore/dgeIntegration.ts new file mode 100644 index 000000000..2875bf5eb --- /dev/null +++ b/src/datastore/dgeIntegration.ts @@ -0,0 +1,307 @@ +/** + * Bridges DGERunner with MDV's live ChartManager/DataStore infrastructure. + * + * Resolves gene fields from rows_as_columns links, loads columns in batches, + * runs the DGE pipeline, and writes result columns back to the genes DataStore. + */ + +import type { DGEConfig, DGERunResult, EffectSizeLabel } from "./DGEDimension"; +import { DGERunner } from "./DGEDimension"; +import { clampEffectSize } from "./dgeStats"; +import { getRowsAsColumnsLinks, getFieldName } from "../links/link_utils"; +import { DEFAULT_DGE_BATCH_SIZE } from "../lib/constants"; + +export type DGEChartManager = { + dsIndex: Record; + dataSources: { name: string; dataStore: any }[]; + loadColumnSetAsync(columns: string[], dataSource: string): Promise; + addChart(dataSource: string, config: Record, notify?: boolean): Promise; +}; + +export interface DGEIntegrationConfig { + groupColumn: string; + targetGroup: string; + referenceGroup: string | "rest"; + batchSize?: number; +} + +export interface DGEIntegrationResult extends DGERunResult { + cellsDsName: string; + genesDsName: string; +} + +export interface DGEIntegrationRunOptions { + signal?: AbortSignal; +} + +/** + * Run DGE analysis using live MDV DataStore infrastructure. + * + * 1. Resolves gene fields from the rows_as_columns link + * 2. Detects whether expression data is log1p or z-scored + * 3. Runs the DGE pipeline via DGERunner + * 4. Writes result columns to the genes DataStore + */ +export async function runDGEOnDataStore( + chartManager: DGEChartManager, + cellsDsName: string, + genesDsName: string, + config: DGEIntegrationConfig, + onProgress?: (done: number, total: number) => void, + options?: DGEIntegrationRunOptions, +): Promise { + const throwIfAborted = () => { + if (options?.signal?.aborted) { + throw new DOMException("DGE run aborted", "AbortError"); + } + }; + + throwIfAborted(); + const cellsDs = chartManager.dsIndex[cellsDsName]?.dataStore; + const genesDs = chartManager.dsIndex[genesDsName]?.dataStore; + if (!cellsDs) throw new Error(`DataStore "${cellsDsName}" not found`); + if (!genesDs) throw new Error(`DataStore "${genesDsName}" not found`); + + // Load group column + await chartManager.loadColumnSetAsync([config.groupColumn], cellsDsName); + throwIfAborted(); + const groupCol = cellsDs.columnIndex[config.groupColumn]; + if (!groupCol) throw new Error(`Group column "${config.groupColumn}" not found`); + if (!groupCol.values) throw new Error(`Group column "${config.groupColumn}" is not categorical`); + + // Normalize group assignments into a Uint8Array SharedArrayBuffer. + // Text columns use Uint8Array, text16 columns use Uint16Array — we need + // a consistent Uint8Array for the DGE worker regardless of source type. + const nCells = cellsDs.size as number; + const groupSAB = new SharedArrayBuffer(nCells); + const groupArr = new Uint8Array(groupSAB); + if (groupCol.datatype === "text16") { + const src = new Uint16Array(groupCol.buffer); + for (let i = 0; i < nCells; i++) groupArr[i] = src[i]; + } else { + const src = new Uint8Array(groupCol.buffer); + groupArr.set(src); + } + + // Resolve gene fields from the rows_as_columns link + const racLinks = getRowsAsColumnsLinks(cellsDs); + if (racLinks.length === 0) { + throw new Error(`No rows_as_columns link found for "${cellsDsName}"`); + } + const selectedLink = racLinks.find((entry) => entry.linkedDs.name === genesDsName); + if (!selectedLink) { + throw new Error(`No rows_as_columns link found from "${cellsDsName}" to "${genesDsName}"`); + } + const { linkedDs, link } = selectedLink; + await link.initPromise; + throwIfAborted(); + + if (!link.valueToRowIndex) { + throw new Error("Link valueToRowIndex not initialized"); + } + + // Build gene field names and corresponding gene names from the linked DataStore + const nameCol = linkedDs.dataStore.columnIndex[link.name_column]; + if (!nameCol?.data) { + throw new Error(`Name column "${link.name_column}" not loaded in genes DataStore`); + } + + const sgKey = Object.keys(link.subgroups)[0]; + const geneNames: string[] = []; + const geneFields: string[] = []; + const geneRowIndices: number[] = []; + + for (const [geneName, rowIndex] of link.valueToRowIndex) { + geneNames.push(geneName); + geneFields.push(getFieldName(sgKey, geneName, rowIndex)); + geneRowIndices.push(rowIndex); + } + + // ── Diagnostics: group column ── + const groupValues: string[] = groupCol.values; + const targetIdx = groupValues.indexOf(config.targetGroup); + const refIdx = config.referenceGroup === "rest" ? -1 : groupValues.indexOf(config.referenceGroup); + const groupCounts = new Map(); + for (let i = 0; i < nCells; i++) { + groupCounts.set(groupArr[i], (groupCounts.get(groupArr[i]) ?? 0) + 1); + } + const filterArr = new Uint8Array(cellsDs.filterBuffer); + let filteredCount = 0; + for (let i = 0; i < nCells; i++) if (filterArr[i] !== 0) filteredCount++; + console.log("[DGE diag] Group column:", config.groupColumn, "datatype:", groupCol.datatype); + console.log("[DGE diag] Group values:", groupValues); + console.log("[DGE diag] Target:", config.targetGroup, `(idx=${targetIdx})`, "Reference:", config.referenceGroup, `(idx=${refIdx})`); + console.log("[DGE diag] Group distribution:", Object.fromEntries([...groupCounts].map(([k, v]) => [groupValues[k] ?? `?${k}`, v]))); + console.log("[DGE diag] Total cells:", nCells, "Filtered out:", filteredCount, "Active:", nCells - filteredCount); + console.log("[DGE diag] Total genes:", geneFields.length); + + // Determine whether expression data is log1p-normalized, linear, or z-scored. + // Prefer the subgroup storage type from datasources.json metadata: + // sparse → data preserves zero structure → log1p or linear (z-scoring densifies) + // dense / missing → ambiguous, fall back to probing gene values + const sgType = link.subgroups[sgKey]?.type as string | undefined; + let dataType: "log1p" | "linear" | "zscored" = "log1p"; + + if (sgType === "sparse") { + // Even for sparse, we probe to distinguish log1p vs linear + console.log("[DGE diag] sgtype=sparse -> probing genes to distinguish log1p vs linear"); + } else { + console.log(`[DGE diag] sgtype=${sgType ?? "undefined"} -> probing genes for detection`); + } + + const MAX_PROBE = Math.min(50, geneFields.length); + const probeFields = geneFields.slice(0, MAX_PROBE); + await chartManager.loadColumnSetAsync(probeFields, cellsDsName); + throwIfAborted(); + + let globalMaxVal = -Infinity; + let globalHasNegative = false; + let anyFinite = false; + + for (let gi = 0; gi < probeFields.length; gi++) { + const col = cellsDs.columnIndex[probeFields[gi]]; + const buf = col?.buffer as SharedArrayBuffer | null; + if (!buf) continue; + const arr = new Float32Array(buf); + for (let j = 0; j < arr.length; j++) { + const v = arr[j]; + if (Number.isNaN(v)) continue; + anyFinite = true; + if (v < 0) { globalHasNegative = true; break; } + if (v > globalMaxVal) globalMaxVal = v; + } + if (globalHasNegative || globalMaxVal > 20) break; + } + + if (!anyFinite) { + console.warn(`[DGE diag] All ${MAX_PROBE} probed genes were all-NaN; defaulting to dataType=log1p`); + } else if (globalHasNegative) { + dataType = "zscored"; + } else { + dataType = globalMaxVal > 20 ? "linear" : "log1p"; + } + console.log(`[DGE diag] Data type detection: globalMax=${globalMaxVal.toFixed(1)}, hasNeg=${globalHasNegative} -> dataType=${dataType}`); + + const dgeConfig: DGEConfig = { + groupColumn: config.groupColumn, + targetGroup: config.targetGroup, + referenceGroup: config.referenceGroup, + // Adjust this parameter to control the DGE batch size (number of genes per request). + // Higher values (e.g. 500-1000) are faster but use more memory per batch. + batchSize: config.batchSize ?? DEFAULT_DGE_BATCH_SIZE, + }; + + const runner = new DGERunner(); + try { + const result = await runner.run( + dgeConfig, + cellsDs.filterBuffer as SharedArrayBuffer, + nCells, + groupSAB, + groupValues, + geneFields, + geneNames, + async (fields: string[]) => { + await chartManager.loadColumnSetAsync(fields, cellsDsName); + }, + (field: string) => { + const col = cellsDs.columnIndex[field]; + return (col?.buffer as SharedArrayBuffer) ?? null; + }, + onProgress, + false, + dataType, + options?.signal, + ); + throwIfAborted(); + + writeResultsToGenesDS(genesDs, result, link.valueToRowIndex); + + return { + ...result, + cellsDsName, + genesDsName, + }; + } finally { + runner.destroy(); + } +} + +/** + * Write DGE result columns into the genes DataStore, mapping results back + * to the correct row order using the gene name → row index map. + */ +function writeResultsToGenesDS( + genesDs: any, + result: DGERunResult, + valueToRowIndex: Map, +): void { + const n = genesDs.size as number; + const esLabel = result.effectSizeLabel; + const esColumnName = esLabel === "log2fc" ? "dge_log2fc" : "dge_mean_diff"; + + const columns: { field: string; name: string; data: Float32Array }[] = [ + { field: esColumnName, name: esLabel === "log2fc" ? "Log2 FC" : "Mean Diff", data: new Float32Array(n).fill(NaN) }, + { field: "dge_effect_size", name: "Effect Size", data: new Float32Array(n).fill(NaN) }, + { field: "dge_pval", name: "P-value", data: new Float32Array(n).fill(NaN) }, + { field: "dge_pval_adj", name: "Adj. P-value", data: new Float32Array(n).fill(NaN) }, + { field: "dge_neg_log10_pval", name: "-log10(p)", data: new Float32Array(n).fill(NaN) }, + { field: "dge_neg_log10_pval_adj", name: "-log10(padj)", data: new Float32Array(n).fill(NaN) }, + ]; + + for (const gene of result.results) { + const rowIdx = valueToRowIndex.get(gene.gene); + if (rowIdx === undefined) continue; + + const clampedES = clampEffectSize(gene.effectSize); + columns[0].data[rowIdx] = clampedES; + columns[1].data[rowIdx] = clampedES; + columns[2].data[rowIdx] = gene.pval; + columns[3].data[rowIdx] = gene.pvalAdj; + columns[4].data[rowIdx] = gene.negLog10Pval; + columns[5].data[rowIdx] = gene.negLog10PvalAdj; + } + + const addedFields: string[] = []; + for (const col of columns) { + const buffer = new SharedArrayBuffer(col.data.byteLength); + new Float32Array(buffer).set(col.data); + + if (genesDs.columnIndex[col.field]) { + genesDs.setColumnData(col.field, buffer); + } else { + genesDs.addColumn( + { name: col.name, field: col.field, datatype: "double" }, + buffer, + true, + ); + } + addedFields.push(col.field); + } + + genesDs.dataChanged(addedFields, true); +} + +/** + * Find which datasource pairs have a rows_as_columns link, + * returning the cells DS name and linked genes DS name. + */ +export function findDGECapableDatasources( + chartManager: DGEChartManager, +): { cellsDsName: string; genesDsName: string }[] { + const results: { cellsDsName: string; genesDsName: string }[] = []; + for (const ds of chartManager.dataSources) { + const dataStore = ds.dataStore; + if (!dataStore.links) continue; + for (const linkedDsName of Object.keys(dataStore.links)) { + const linkSet = dataStore.links[linkedDsName]; + if (linkSet.rows_as_columns) { + results.push({ + cellsDsName: ds.name, + genesDsName: linkedDsName, + }); + } + } + } + return results; +} diff --git a/src/datastore/dgeStats.ts b/src/datastore/dgeStats.ts new file mode 100644 index 000000000..39aa3505a --- /dev/null +++ b/src/datastore/dgeStats.ts @@ -0,0 +1,335 @@ +/** + * mdv-dge: Pure statistical functions for browser-based differential gene expression. + * + * All functions are pure (no side effects, no DOM, no SharedArrayBuffer dependency) + * so they can be used in Web Workers, tests, or the main thread. + */ + +// ── Welford's Online Algorithm ────────────────────────────────────────────── + +export interface WelfordAccumulator { + n: number; + mean: number; + m2: number; +} + +export interface GroupStats { + mean: number; + variance: number; + n: number; +} + +export function welfordCreate(): WelfordAccumulator { + return { n: 0, mean: 0, m2: 0 }; +} + +export function welfordAccumulate(acc: WelfordAccumulator, value: number): void { + acc.n++; + const delta = value - acc.mean; + acc.mean += delta / acc.n; + const delta2 = value - acc.mean; + acc.m2 += delta * delta2; +} + +export function welfordFinalize(acc: WelfordAccumulator): GroupStats { + if (acc.n < 2) { + return { mean: acc.mean, variance: 0, n: acc.n }; + } + return { mean: acc.mean, variance: acc.m2 / (acc.n - 1), n: acc.n }; +} + +// ── t-Distribution CDF ───────────────────────────────────────────────────── + +/** + * Regularized incomplete beta function I_x(a, b) via continued fraction + * (Lentz's algorithm). Used for the t-distribution CDF. + */ +function betaIncomplete(x: number, a: number, b: number): number { + if (x <= 0) return 0; + if (x >= 1) return 1; + + // Use the symmetry relation when x > (a+1)/(a+b+2) for convergence + if (x > (a + 1) / (a + b + 2)) { + return 1 - betaIncomplete(1 - x, b, a); + } + + const lnBeta = lnGamma(a) + lnGamma(b) - lnGamma(a + b); + const front = Math.exp(Math.log(x) * a + Math.log(1 - x) * b - lnBeta) / a; + + // Lentz's continued fraction + const TINY = 1e-30; + const EPS = 1e-14; + let f = TINY; + let c = 1; + let d = 1 - (a + b) * x / (a + 1); + if (Math.abs(d) < TINY) d = TINY; + d = 1 / d; + f = d; + + for (let m = 1; m <= 200; m++) { + // Even step: a_2m + let numerator = m * (b - m) * x / ((a + 2 * m - 1) * (a + 2 * m)); + d = 1 + numerator * d; + if (Math.abs(d) < TINY) d = TINY; + c = 1 + numerator / c; + if (Math.abs(c) < TINY) c = TINY; + d = 1 / d; + f *= c * d; + + // Odd step: a_2m+1 + numerator = -(a + m) * (a + b + m) * x / ((a + 2 * m) * (a + 2 * m + 1)); + d = 1 + numerator * d; + if (Math.abs(d) < TINY) d = TINY; + c = 1 + numerator / c; + if (Math.abs(c) < TINY) c = TINY; + d = 1 / d; + const delta = c * d; + f *= delta; + + if (Math.abs(delta - 1) < EPS) break; + } + + return front * f; +} + +/** + * Log-gamma function using Lanczos approximation (g=7, n=9). + */ +function lnGamma(z: number): number { + if (z < 0.5) { + return Math.log(Math.PI / Math.sin(Math.PI * z)) - lnGamma(1 - z); + } + z -= 1; + const c = [ + 0.99999999999980993, + 676.5203681218851, + -1259.1392167224028, + 771.32342877765313, + -176.61502916214059, + 12.507343278686905, + -0.13857109526572012, + 9.9843695780195716e-6, + 1.5056327351493116e-7, + ]; + let x = c[0]; + for (let i = 1; i < 9; i++) { + x += c[i] / (z + i); + } + const t = z + 7.5; + return 0.5 * Math.log(2 * Math.PI) + (z + 0.5) * Math.log(t) - t + Math.log(x); +} + +/** + * Two-sided p-value from Student's t-distribution. + */ +export function tDistPValue(t: number, df: number): number { + if (df <= 0 || !Number.isFinite(t) || !Number.isFinite(df)) return NaN; + if (df === Number.POSITIVE_INFINITY) { + // Normal distribution limit + return 2 * (1 - normalCDF(Math.abs(t))); + } + const x = df / (df + t * t); + const ibeta = betaIncomplete(x, df / 2, 0.5); + return ibeta; +} + +/** Standard normal CDF (for the df→∞ case). */ +function normalCDF(x: number): number { + const a1 = 0.254829592; + const a2 = -0.284496736; + const a3 = 1.421413741; + const a4 = -1.453152027; + const a5 = 1.061405429; + const p = 0.3275911; + const sign = x < 0 ? -1 : 1; + x = Math.abs(x) / Math.SQRT2; + const t = 1 / (1 + p * x); + const y = 1 - ((((a5 * t + a4) * t + a3) * t + a2) * t + a1) * t * Math.exp(-x * x); + return 0.5 * (1 + sign * y); +} + +// ── Welch's t-Test ────────────────────────────────────────────────────────── + +export interface TTestResult { + tStatistic: number; + degreesOfFreedom: number; + pValue: number; +} + +/** + * Welch's t-test from pre-computed group statistics. + * Returns NaN p-value if either group has < 2 observations or zero variance. + */ +export function welchTTest(g1: GroupStats, g2: GroupStats): TTestResult { + if (g1.n < 2 || g2.n < 2) { + return { tStatistic: NaN, degreesOfFreedom: NaN, pValue: NaN }; + } + + const v1 = g1.variance / g1.n; + const v2 = g2.variance / g2.n; + const vSum = v1 + v2; + + if (vSum === 0) { + if (g1.mean === g2.mean) { + return { tStatistic: 0, degreesOfFreedom: g1.n + g2.n - 2, pValue: 1 }; + } + // Perfect separation: means differ but zero variance in both groups + const sign = g1.mean > g2.mean ? 1 : -1; + return { tStatistic: sign * Infinity, degreesOfFreedom: g1.n + g2.n - 2, pValue: 0 }; + } + + const t = (g1.mean - g2.mean) / Math.sqrt(vSum); + const df = (vSum * vSum) / (v1 * v1 / (g1.n - 1) + v2 * v2 / (g2.n - 1)); + const pValue = tDistPValue(t, df); + + return { tStatistic: t, degreesOfFreedom: df, pValue }; +} + +// ── Effect Size / Fold Change ──────────────────────────────────────────────── + +const PSEUDOCOUNT = 1e-6; + +/** + * Log2 fold change matching Scanpy's formula for log1p-normalized data: + * log2((expm1(meanTarget) + 1e-6) / (expm1(meanRef) + 1e-6)) + */ +export function log2FoldChange(meanTarget: number, meanReference: number, isLog1p: boolean): number { + const valT = isLog1p ? Math.expm1(meanTarget) : meanTarget; + const valR = isLog1p ? Math.expm1(meanReference) : meanReference; + const numFC = Math.max(0, valT) + PSEUDOCOUNT; + const denFC = Math.max(0, valR) + PSEUDOCOUNT; + return Math.log2(numFC / denFC); +} + +/** Clamp extreme effect sizes for display/storage. */ +export const MAX_EFFECT_SIZE = 10; +export function clampEffectSize(value: number): number { + if (Number.isNaN(value)) return NaN; + return Math.max(-MAX_EFFECT_SIZE, Math.min(MAX_EFFECT_SIZE, value)); +} + +/** + * Adaptive effect size that handles log1p-normalized, linear, and z-scored data. + * + * - Log1p/Linear data (non-negative): Log2 fold change + * - Z-scored data (has negatives): simple mean difference (target - reference) + */ +export function computeEffectSize(meanTarget: number, meanReference: number, dataType: "log1p" | "linear" | "zscored"): number { + if (dataType === "zscored") { + return meanTarget - meanReference; + } + return log2FoldChange(meanTarget, meanReference, dataType === "log1p"); +} + +/** + * Detect the data type by examining non-NaN values from a Float32Array. + * + * Returns null if all values are NaN (inconclusive). + */ +export function detectDataType(values: Float32Array): "log1p" | "linear" | "zscored" | null { + let hasFinite = false; + let hasNegative = false; + let maxVal = -Infinity; + + for (let i = 0; i < values.length; i++) { + const v = values[i]; + if (Number.isNaN(v)) continue; + hasFinite = true; + if (v < 0) hasNegative = true; + if (v > maxVal) maxVal = v; + } + + if (!hasFinite) return null; + if (hasNegative) return "zscored"; + // Heuristic: log1p-normalized data rarely has values > 20 (e^20 is huge) + return maxVal > 20 ? "linear" : "log1p"; +} + +// ── Benjamini-Hochberg Correction ─────────────────────────────────────────── + +/** + * Benjamini-Hochberg multiple testing correction. + * Returns adjusted p-values in the same order as input. + */ +export function benjaminiHochberg(pvalues: number[]): number[] { + const n = pvalues.length; + if (n === 0) return []; + + const indexed = pvalues.map((p, i) => ({ p, i })); + indexed.sort((a, b) => a.p - b.p); + + const adjusted = new Array(n); + let cumMin = 1; + + for (let rank = n - 1; rank >= 0; rank--) { + const corrected = (indexed[rank].p * n) / (rank + 1); + cumMin = Math.min(cumMin, corrected); + adjusted[indexed[rank].i] = Math.min(1, cumMin); + } + + return adjusted; +} + +// ── Per-Gene DGE Computation ──────────────────────────────────────────────── + +export interface GeneResult { + gene: string; + effectSize: number; + pval: number; + meanTarget: number; + meanReference: number; + tStatistic: number; + degreesOfFreedom: number; +} + +/** + * Compute DGE statistics for a single gene from its expression values. + * + * @param geneName Gene identifier + * @param values Float32Array of expression values + * @param groupAssignments Uint8Array of group indices per cell + * @param filterArray Uint8Array where 0 = visible, >0 = filtered out + * @param targetGroup Index of the target group + * @param referenceGroup Index of reference group, or -1 for "rest" + * @param dataType Type of data (log1p, linear, or zscored) + */ +export function computeGeneStats( + geneName: string, + values: Float32Array, + groupAssignments: Uint8Array, + filterArray: Uint8Array, + targetGroup: number, + referenceGroup: number, + dataType: "log1p" | "linear" | "zscored" = "log1p", +): GeneResult { + const accTarget = welfordCreate(); + const accRef = welfordCreate(); + const isRest = referenceGroup < 0; + + for (let i = 0; i < values.length; i++) { + if (filterArray[i] !== 0) continue; + // NaN in sparse columns means zero expression, not missing data + const val = Number.isNaN(values[i]) ? 0 : values[i]; + + const group = groupAssignments[i]; + if (group === targetGroup) { + welfordAccumulate(accTarget, val); + } else if (isRest || group === referenceGroup) { + welfordAccumulate(accRef, val); + } + } + + const statsTarget = welfordFinalize(accTarget); + const statsRef = welfordFinalize(accRef); + const ttest = welchTTest(statsTarget, statsRef); + + return { + gene: geneName, + effectSize: computeEffectSize(statsTarget.mean, statsRef.mean, dataType), + pval: ttest.pValue, + meanTarget: statsTarget.mean, + meanReference: statsRef.mean, + tStatistic: ttest.tStatistic, + degreesOfFreedom: ttest.degreesOfFreedom, + }; +} diff --git a/src/datastore/dgeWorker.ts b/src/datastore/dgeWorker.ts new file mode 100644 index 000000000..cc0fc0db4 --- /dev/null +++ b/src/datastore/dgeWorker.ts @@ -0,0 +1,70 @@ +/** + * mdv-dge Web Worker: processes gene expression batches and computes + * per-gene DGE statistics using sufficient statistics (Welford + Welch t-test). + * + * Protocol: + * Main thread sends "run_batch" messages with SharedArrayBuffers. + * Worker responds with "batch_result" containing per-gene stats. + * + * All heavy computation is delegated to dgeStats.ts pure functions. + */ + +import { computeGeneStats } from "./dgeStats"; +import type { GeneResult } from "./dgeStats"; + +export interface DGEBatchInput { + type: "run_batch"; + filterBuffer: SharedArrayBuffer; + groupBuffer: SharedArrayBuffer; + targetGroup: number; + referenceGroup: number; // -1 means "rest" + geneBuffers: SharedArrayBuffer[]; + geneNames: string[]; + batchIndex: number; + dataType: "log1p" | "linear" | "zscored"; +} + +export interface DGEBatchOutput { + type: "batch_result"; + batchIndex: number; + results: GeneResult[]; +} + +/** + * Core batch computation, exported for direct use in tests. + */ +export function processBatch(msg: DGEBatchInput): DGEBatchOutput { + const filterArray = new Uint8Array(msg.filterBuffer); + const groupAssignments = new Uint8Array(msg.groupBuffer); + + const results: GeneResult[] = []; + for (let i = 0; i < msg.geneNames.length; i++) { + const values = new Float32Array(msg.geneBuffers[i]); + results.push( + computeGeneStats( + msg.geneNames[i], + values, + groupAssignments, + filterArray, + msg.targetGroup, + msg.referenceGroup, + msg.dataType, + ), + ); + } + + return { + type: "batch_result", + batchIndex: msg.batchIndex, + results, + }; +} + +// Worker message handler -- only active when running as a Worker +// biome-ignore lint/suspicious/noGlobalAssign: Web Worker entry point +onmessage = (e: MessageEvent) => { + if (e.data.type === "run_batch") { + const result = processBatch(e.data); + postMessage(result); + } +}; diff --git a/src/lib/constants.ts b/src/lib/constants.ts index 04af77c89..32acee284 100644 --- a/src/lib/constants.ts +++ b/src/lib/constants.ts @@ -1,2 +1,3 @@ export const AUTOCOMPLETE_OPTIONS_LIMIT = 500; -export const AUTOCOMPLETE_TAGS_LIMIT = 100; \ No newline at end of file +export const AUTOCOMPLETE_TAGS_LIMIT = 100; +export const DEFAULT_DGE_BATCH_SIZE = 2000; \ No newline at end of file diff --git a/src/tests/datastore-setColumnData.spec.ts b/src/tests/datastore-setColumnData.spec.ts new file mode 100644 index 000000000..97262f37e --- /dev/null +++ b/src/tests/datastore-setColumnData.spec.ts @@ -0,0 +1,70 @@ +import { beforeEach, describe, expect, test, vi } from "vitest"; + +// DataStore imports helpers via @/lib/utils +vi.mock("@/lib/utils", async (importOriginal) => { + const actual: any = await importOriginal(); + return { + ...actual, + isDatatypeNumeric: (t: string) => !!t.match(/double|float|int/), + }; +}); + +beforeEach(() => { + // DataStore uses a WebWorker in its constructor; Node test env doesn't provide it. + // A minimal stub is enough for these minMax/quantiles tests. + vi.stubGlobal( + "Worker", + class WorkerStub { + onmessage = null; + onerror = null; + postMessage(_msg: any) {} + terminate() {} + addEventListener(_type: any, _listener: any) {} + removeEventListener(_type: any, _listener: any) {} + }, + ); +}); + +function f32Buffer(values: number[]): ArrayBuffer { + const buf = new ArrayBuffer(values.length * 4); + new Float32Array(buf).set(values); + return buf; +} + +describe("DataStore.setColumnData (numeric stats)", () => { + test("recomputes minMax/quantiles by default when overwriting numeric data", async () => { + // Import after stubbing Worker + const { default: DataStore } = await import("../datastore/DataStore.js"); + const ds = new DataStore(3, { + columns: [{ name: "x", field: "x", datatype: "double" }], + }); + + ds.setColumnData("x", f32Buffer([10, 11, 12])); + expect(ds.getMinMaxForColumn("x")).toEqual([10, 12]); + + // Overwrite with new data on the same column (DGE-style behavior) + ds.setColumnData("x", f32Buffer([-5, 0, 5])); + expect(ds.getMinMaxForColumn("x")).toEqual([-5, 5]); + // Quantiles should also have been recomputed (existence check) + expect(ds.columnIndex.x.quantiles).toBeTruthy(); + }); + + test("can preserve existing stats when recomputeStats=false", async () => { + // Import after stubbing Worker + const { default: DataStore } = await import("../datastore/DataStore.js"); + const ds = new DataStore(3, { + columns: [{ name: "x", field: "x", datatype: "double" }], + }); + + ds.setColumnData("x", f32Buffer([1, 2, 3])); + expect(ds.getMinMaxForColumn("x")).toEqual([1, 3]); + + // Force a stale minMax to simulate a caller intentionally setting it + ds.columnIndex.x.minMax = [100, 200]; + ds.setColumnData("x", f32Buffer([7, 8, 9]), { recomputeStats: false }); + + // With recomputeStats disabled, we preserve the existing minMax cache. + expect(ds.getMinMaxForColumn("x")).toEqual([100, 200]); + }); +}); + diff --git a/src/tests/dge-integration.spec.ts b/src/tests/dge-integration.spec.ts new file mode 100644 index 000000000..64c2b97c0 --- /dev/null +++ b/src/tests/dge-integration.spec.ts @@ -0,0 +1,406 @@ +import { describe, expect, test, vi, beforeEach, afterEach } from "vitest"; +import { + computeEffectSize, + log2FoldChange, + computeGeneStats, +} from "@/datastore/dgeStats"; +import { runDGEOnDataStore } from "@/datastore/dgeIntegration"; +import * as linkUtils from "@/links/link_utils"; +import { DGERunner } from "@/datastore/DGEDimension"; + +// ── Data type detection logic ─────────────────────────────────────────────── +// Mirrors the multi-gene probe loop in dgeIntegration.ts: runDGEOnDataStore. +// The real code scans values across multiple genes, tracking globalMaxVal and +// globalHasNegative, and only makes a decision after examining all probes (or +// early-exits on a definitive signal). + +function resolveDataTypeMultiGene( + geneArrays: (Float32Array | null)[], +): "log1p" | "linear" | "zscored" { + let globalMaxVal = -Infinity; + let globalHasNegative = false; + let anyFinite = false; + + for (const arr of geneArrays) { + if (!arr) continue; + for (let j = 0; j < arr.length; j++) { + const v = arr[j]; + if (Number.isNaN(v)) continue; + anyFinite = true; + if (v < 0) { globalHasNegative = true; break; } + if (v > globalMaxVal) globalMaxVal = v; + } + if (globalHasNegative || globalMaxVal > 20) break; + } + + if (!anyFinite) return "log1p"; + if (globalHasNegative) return "zscored"; + return globalMaxVal > 20 ? "linear" : "log1p"; +} + +describe("multi-gene data type detection", () => { + test("detects linear when LATER genes have high values (raw count data)", () => { + const gene0 = new Float32Array([NaN, NaN, NaN, NaN]); + const gene1 = new Float32Array([NaN, 1, NaN, 2]); + const gene2 = new Float32Array([NaN, NaN, 3, NaN]); + const gene3 = new Float32Array([NaN, 50, NaN, 116]); + expect(resolveDataTypeMultiGene([gene0, gene1, gene2, gene3])).toBe("linear"); + }); + + test("first gene with low values does NOT prematurely decide log1p", () => { + const gene0 = new Float32Array([NaN, 2.0, NaN, NaN]); + const gene1 = new Float32Array([NaN, NaN, NaN, NaN]); + const gene2 = new Float32Array([NaN, 100, NaN, NaN]); + expect(resolveDataTypeMultiGene([gene0, gene1, gene2])).toBe("linear"); + }); + + test("detects log1p when ALL genes have small values", () => { + const gene0 = new Float32Array([NaN, 0.5, NaN, 1.2]); + const gene1 = new Float32Array([NaN, NaN, 3.1, NaN]); + expect(resolveDataTypeMultiGene([gene0, gene1])).toBe("log1p"); + }); + + test("detects zscored immediately on negative value", () => { + const gene0 = new Float32Array([NaN, -0.5, NaN, 2.3]); + const gene1 = new Float32Array([NaN, 50, NaN, NaN]); + expect(resolveDataTypeMultiGene([gene0, gene1])).toBe("zscored"); + }); + + test("all-NaN across all genes defaults to log1p", () => { + const gene0 = new Float32Array([NaN, NaN, NaN]); + const gene1 = new Float32Array([NaN, NaN]); + expect(resolveDataTypeMultiGene([gene0, gene1])).toBe("log1p"); + }); + + test("null buffers are skipped gracefully", () => { + const gene0 = null; + const gene1 = new Float32Array([NaN, 25, NaN]); + expect(resolveDataTypeMultiGene([gene0, gene1])).toBe("linear"); + }); +}); + +describe("computeEffectSize", () => { + test("uses log2FC formula for log1p data", () => { + const result = computeEffectSize(2.0, 1.0, "log1p"); + const expected = log2FoldChange(2.0, 1.0, true); + expect(result).toBe(expected); + }); + + test("uses log2FC formula for linear data (without expm1)", () => { + const result = computeEffectSize(2.0, 1.0, "linear"); + const expected = log2FoldChange(2.0, 1.0, false); + expect(result).toBe(expected); + }); + + test("uses mean difference for z-scored data", () => { + const result = computeEffectSize(2.0, 1.0, "zscored"); + expect(result).toBe(1.0); + }); + + test("mean difference works with negative values (z-scored)", () => { + const result = computeEffectSize(-0.5, 0.3, "zscored"); + expect(result).toBeCloseTo(-0.8, 10); + }); + + test("mean difference is zero when means are equal", () => { + expect(computeEffectSize(3.5, 3.5, "zscored")).toBe(0); + }); + + test("log2FC is zero when means are equal", () => { + expect(computeEffectSize(1.5, 1.5, "log1p")).toBeCloseTo(0, 6); + }); + + test("log2FC is positive when target > reference (log1p data)", () => { + expect(computeEffectSize(3.0, 1.0, "log1p")).toBeGreaterThan(0); + }); + + test("mean diff is positive when target > reference (z-scored data)", () => { + expect(computeEffectSize(3.0, 1.0, "zscored")).toBeGreaterThan(0); + }); +}); + +describe("computeGeneStats with dataType parameter", () => { + function makeArrays(values: number[], groups: number[]) { + return { + v: new Float32Array(values), + g: new Uint8Array(groups), + f: new Uint8Array(values.length), + }; + } + + test("uses log2FC for log1p data type", () => { + const values = [2, 3, 4, 5, 6, 10, 11, 12, 13, 14]; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]; + const { v, g, f } = makeArrays(values, groups); + + const result = computeGeneStats("Gene1", v, g, f, 0, -1, "log1p"); + const expectedEffectSize = log2FoldChange(4, 12, true); + expect(result.effectSize).toBeCloseTo(expectedEffectSize, 3); + }); + + test("uses log2FC for linear data type (without expm1)", () => { + const values = [2, 3, 4, 5, 6, 10, 11, 12, 13, 14]; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]; + const { v, g, f } = makeArrays(values, groups); + + const result = computeGeneStats("Gene1", v, g, f, 0, -1, "linear"); + const expectedEffectSize = log2FoldChange(4, 12, false); + expect(result.effectSize).toBeCloseTo(expectedEffectSize, 3); + }); + + test("uses mean difference for zscored data type", () => { + const values = [-1, -0.5, 0, 0.5, 1, 2, 2.5, 3, 3.5, 4]; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]; + const { v, g, f } = makeArrays(values, groups); + + const result = computeGeneStats("Gene1", v, g, f, 0, -1, "zscored"); + expect(result.effectSize).toBeCloseTo(0 - 3, 3); + }); + + test("effect size sign is consistent across data types", () => { + const values = [1, 1, 1, 1, 1, 5, 5, 5, 5, 5]; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]; + const { v, g, f } = makeArrays(values, groups); + + const resultLog1p = computeGeneStats("Gene1", v, g, f, 0, -1, "log1p"); + const resultLinear = computeGeneStats("Gene1", v, g, f, 0, -1, "linear"); + const resultZscored = computeGeneStats("Gene1", v, g, f, 0, -1, "zscored"); + + expect(resultLog1p.effectSize).toBeLessThan(0); + expect(resultLinear.effectSize).toBeLessThan(0); + expect(resultZscored.effectSize).toBeLessThan(0); + }); +}); + +describe("gene ordering alignment", () => { + test("maps DGE results to correct gene row indices", () => { + const valueToRowIndex = new Map([ + ["GeneA", 0], + ["GeneB", 1], + ["GeneC", 2], + ["GeneD", 3], + ["GeneE", 4], + ]); + const n = 5; + + const dgeResults = [ + { gene: "GeneD", effectSize: 2.5, pval: 0.001 }, + { gene: "GeneA", effectSize: -1.0, pval: 0.01 }, + { gene: "GeneC", effectSize: 0.5, pval: 0.05 }, + ]; + + const effectSizeColumn = new Float32Array(n).fill(NaN); + const pvalColumn = new Float32Array(n).fill(NaN); + + for (const result of dgeResults) { + const idx = valueToRowIndex.get(result.gene); + if (idx !== undefined) { + effectSizeColumn[idx] = result.effectSize; + pvalColumn[idx] = result.pval; + } + } + + expect(effectSizeColumn[0]).toBeCloseTo(-1.0, 5); + expect(pvalColumn[0]).toBeCloseTo(0.01, 5); + expect(effectSizeColumn[1]).toBeNaN(); + expect(pvalColumn[1]).toBeNaN(); + expect(effectSizeColumn[2]).toBeCloseTo(0.5, 5); + expect(effectSizeColumn[3]).toBeCloseTo(2.5, 5); + expect(effectSizeColumn[4]).toBeNaN(); + }); + + test("handles duplicate gene names by using last occurrence", () => { + const valueToRowIndex = new Map([ + ["Gene1", 0], + ["Gene2", 1], + ]); + + const dgeResults = [ + { gene: "Gene1", effectSize: 1.0 }, + { gene: "Gene1", effectSize: 2.0 }, + ]; + + const column = new Float32Array(2).fill(NaN); + for (const result of dgeResults) { + const idx = valueToRowIndex.get(result.gene); + if (idx !== undefined) { + column[idx] = result.effectSize; + } + } + + expect(column[0]).toBeCloseTo(2.0, 5); + }); + + test("handles genes not in the DataStore gracefully", () => { + const valueToRowIndex = new Map([ + ["Gene1", 0], + ]); + + const column = new Float32Array(1).fill(NaN); + const idx = valueToRowIndex.get("UnknownGene"); + expect(idx).toBeUndefined(); + expect(column[0]).toBeNaN(); + }); +}); + +describe("runDGEOnDataStore link selection", () => { + beforeEach(() => { + vi.restoreAllMocks(); + }); + + afterEach(() => { + vi.restoreAllMocks(); + }); + + function makeGenesDs(size: number) { + const columnIndex: Record = {}; + return { + size, + columnIndex, + addColumn: vi.fn((meta: any, buffer: SharedArrayBuffer) => { + columnIndex[meta.field] = { ...meta, buffer, data: new Float32Array(buffer) }; + }), + setColumnData: vi.fn((field: string, buffer: SharedArrayBuffer) => { + columnIndex[field] = { ...(columnIndex[field] ?? { field }), buffer, data: new Float32Array(buffer) }; + }), + dataChanged: vi.fn(), + }; + } + + test("uses selected genes datasource link when multiple rows_as_columns links exist", async () => { + const nCells = 3; + const groupBuffer = new SharedArrayBuffer(nCells); + new Uint8Array(groupBuffer).set([0, 1, 0]); + const filterBuffer = new SharedArrayBuffer(nCells); + + const cellsDs: any = { + size: nCells, + name: "cells", + columnIndex: { + group: { + datatype: "text", + values: ["target", "ref"], + buffer: groupBuffer, + }, + }, + filterBuffer, + }; + + const genesRna = makeGenesDs(3); + const genesProtein = makeGenesDs(3); + const linksByField: Record = { + rna: { + linkedDs: { name: "rna", dataStore: { columnIndex: { gene_name: { data: new Uint8Array([0, 1, 2]) } } } }, + link: { + name_column: "gene_name", + subgroups: { sg_rna: { type: "sparse", name: "RNA", label: "RNA" } }, + valueToRowIndex: new Map([["GeneA", 0], ["GeneB", 1]]), + initPromise: Promise.resolve(), + }, + }, + protein: { + linkedDs: { name: "protein", dataStore: { columnIndex: { feature_name: { data: new Uint8Array([0, 1, 2]) } } } }, + link: { + name_column: "feature_name", + subgroups: { sg_protein: { type: "sparse", name: "Protein", label: "Protein" } }, + valueToRowIndex: new Map([["GeneA", 2], ["GeneB", 0]]), + initPromise: Promise.resolve(), + }, + }, + }; + + vi.spyOn(linkUtils, "getRowsAsColumnsLinks").mockImplementation(() => [ + linksByField.rna, + linksByField.protein, + ]); + vi.spyOn(linkUtils, "getFieldName").mockImplementation((sg, value, idx) => `${sg}|${value}|${idx}`); + vi.spyOn(DGERunner.prototype, "run").mockResolvedValue({ + results: [ + { + gene: "GeneB", + pval: 0.01, + pvalAdj: 0.02, + negLog10Pval: 2, + negLog10PvalAdj: 1.7, + effectSize: 1.5, + }, + ], + effectSizeLabel: "log2fc", + skippedGenes: 0, + elapsed: 10, + } as any); + vi.spyOn(DGERunner.prototype, "destroy").mockImplementation(() => {}); + + const chartManager: any = { + dsIndex: { + cells: { dataStore: cellsDs, name: "cells" }, + rna: { dataStore: genesRna, name: "rna" }, + protein: { dataStore: genesProtein, name: "protein" }, + }, + loadColumnSetAsync: vi.fn(async (columns: string[]) => { + for (const field of columns) { + if (!cellsDs.columnIndex[field]) { + const buf = new SharedArrayBuffer(nCells * 4); + new Float32Array(buf).set([0, 1, 2]); + cellsDs.columnIndex[field] = { buffer: buf }; + } + } + }), + }; + + await runDGEOnDataStore(chartManager, "cells", "protein", { + groupColumn: "group", + targetGroup: "target", + referenceGroup: "ref", + }); + + const esCol = genesProtein.columnIndex.dge_effect_size.data as Float32Array; + expect(esCol[0]).toBeCloseTo(1.5, 5); + expect(esCol[2]).toBeNaN(); + expect(genesRna.columnIndex.dge_effect_size).toBeUndefined(); + }); + + test("throws clear error when selected genes datasource is not linked", async () => { + const nCells = 2; + const groupBuffer = new SharedArrayBuffer(nCells); + new Uint8Array(groupBuffer).set([0, 1]); + const cellsDs: any = { + size: nCells, + name: "cells", + columnIndex: { + group: { datatype: "text", values: ["A", "B"], buffer: groupBuffer }, + }, + filterBuffer: new SharedArrayBuffer(nCells), + }; + const genesRna = makeGenesDs(2); + + vi.spyOn(linkUtils, "getRowsAsColumnsLinks").mockReturnValue([ + { + linkedDs: { name: "rna", dataStore: { columnIndex: { gene_name: { data: new Uint8Array([0, 1]) } } } }, + link: { + name_column: "gene_name", + subgroups: { sg_rna: { type: "sparse", name: "RNA", label: "RNA" } }, + valueToRowIndex: new Map(), + initPromise: Promise.resolve(), + }, + }, + ] as any); + + const chartManager: any = { + dsIndex: { + cells: { dataStore: cellsDs, name: "cells" }, + rna: { dataStore: genesRna, name: "rna" }, + protein: { dataStore: makeGenesDs(2), name: "protein" }, + }, + loadColumnSetAsync: vi.fn(async () => {}), + }; + + await expect( + runDGEOnDataStore(chartManager, "cells", "protein", { + groupColumn: "group", + targetGroup: "A", + referenceGroup: "B", + }), + ).rejects.toThrow('No rows_as_columns link found from "cells" to "protein"'); + }); +}); diff --git a/src/tests/dge-pbmc3k.spec.ts b/src/tests/dge-pbmc3k.spec.ts new file mode 100644 index 000000000..ac14731c7 --- /dev/null +++ b/src/tests/dge-pbmc3k.spec.ts @@ -0,0 +1,304 @@ +import { describe, expect, test, beforeAll } from "vitest"; +import * as fs from "node:fs"; +import * as path from "node:path"; +import { + welfordCreate, + welfordAccumulate, + welfordFinalize, + welchTTest, + log2FoldChange, + benjaminiHochberg, + computeGeneStats, +} from "@/datastore/dgeStats"; + +/** + * Validates mdv-dge browser-side DGE against Scanpy reference results + * using real PBMC3k data (100 gene subset from 2,638 cells, 9 leiden clusters). + * + * This test does NOT need a browser or Web Worker -- it tests the pure math + * against Scanpy's rank_genes_groups output. + */ + +interface ValidationData { + n_cells: number; + n_genes_total: number; + n_genes_selected: number; + genes: string[]; + leiden: string[]; + expression: Record; +} + +interface ReferenceData { + method: string; + groupby: string; + reference: string; + n_cells: number; + n_genes: number; + group_sizes: Record; + groups: Record< + string, + { + genes: string[]; + scores: number[]; + log2fc: number[]; + pvals: number[]; + pvals_adj: number[]; + } + >; +} + +const FIXTURE_DIR = path.resolve(__dirname, "fixtures"); + +let validationData: ValidationData; +let referenceData: ReferenceData; + +beforeAll(() => { + validationData = JSON.parse( + fs.readFileSync(path.join(FIXTURE_DIR, "dge_validation_data.json"), "utf-8"), + ); + referenceData = JSON.parse( + fs.readFileSync(path.join(FIXTURE_DIR, "dge_reference_ttest.json"), "utf-8"), + ); +}); + +/** + * Run DGE for a specific target cluster vs rest, using the validation expression data. + */ +function runDGEForCluster(targetCluster: string) { + const { leiden, genes, expression } = validationData; + const nCells = leiden.length; + + const filterArray = new Uint8Array(nCells); // all zeros = no filtering + const clusterValues = [...new Set(leiden)].sort(); + const groupAssignments = new Uint8Array( + leiden.map((l) => clusterValues.indexOf(l)), + ); + const targetGroupIdx = clusterValues.indexOf(targetCluster); + + const results = genes.map((gene) => { + const values = new Float32Array(expression[gene]); + return computeGeneStats( + gene, + values, + groupAssignments, + filterArray, + targetGroupIdx, + -1, // "rest" + ); + }); + + const pvals = results.map((r) => r.pval); + const adjustedPvals = benjaminiHochberg(pvals); + + return results.map((r, i) => ({ + ...r, + pvalAdj: adjustedPvals[i], + })); +} + +describe("PBMC3k validation: mdv-dge vs Scanpy t-test", () => { + test("fixture data loads correctly", () => { + expect(validationData.n_cells).toBe(2638); + expect(validationData.n_genes_selected).toBe(100); + expect(validationData.genes).toHaveLength(100); + expect(validationData.leiden).toHaveLength(2638); + + expect(referenceData.method).toBe("t-test"); + expect(referenceData.n_cells).toBe(2638); + }); + + const clustersToTest = ["0", "1", "2", "3"]; + + for (const cluster of clustersToTest) { + describe(`cluster ${cluster} vs rest`, () => { + let mdvResults: ReturnType; + let refGroup: ReferenceData["groups"][string]; + + beforeAll(() => { + mdvResults = runDGEForCluster(cluster); + refGroup = referenceData.groups[cluster]; + }); + + test("log2FC direction agrees for genes with |log2FC| > 0.5", () => { + let testedCount = 0; + let agreementCount = 0; + + for (const mdvGene of mdvResults) { + const refIdx = refGroup.genes.indexOf(mdvGene.gene); + if (refIdx < 0) continue; + const refLog2FC = refGroup.log2fc[refIdx]; + + if (Math.abs(refLog2FC) < 0.5) continue; + testedCount++; + if (Math.sign(mdvGene.effectSize) === Math.sign(refLog2FC)) { + agreementCount++; + } + } + + const agreementRate = testedCount > 0 ? agreementCount / testedCount : 1; + expect(agreementRate).toBeGreaterThanOrEqual(0.95); + }); + + test("log2FC values are within tolerance for shared genes", () => { + let testedCount = 0; + let withinTolerance = 0; + + for (const mdvGene of mdvResults) { + const refIdx = refGroup.genes.indexOf(mdvGene.gene); + if (refIdx < 0) continue; + const refLog2FC = refGroup.log2fc[refIdx]; + testedCount++; + + const absDiff = Math.abs(mdvGene.effectSize - refLog2FC); + // Allow absolute tolerance of 0.15 (Scanpy computes on the full + // normalized matrix while we compute on the same underlying data, + // but Float32 precision and mean-of-log vs log-of-mean differences + // can introduce small discrepancies) + if (absDiff < 0.15 || absDiff / (Math.abs(refLog2FC) + 0.01) < 0.15) { + withinTolerance++; + } + } + + const rate = testedCount > 0 ? withinTolerance / testedCount : 1; + expect(rate).toBeGreaterThanOrEqual(0.9); + }); + + test("p-value ordering broadly agrees (top 20 overlap >= 60%)", () => { + // Sort mdv results by p-value and get top 20 gene names + const mdvTop20 = [...mdvResults] + .filter((r) => !Number.isNaN(r.pval)) + .sort((a, b) => a.pval - b.pval) + .slice(0, 20) + .map((r) => r.gene); + + // Scanpy reference genes are already sorted by p-value + // But only the 100 validation genes are in our analysis + const validationGeneSet = new Set(validationData.genes); + const refTop20 = refGroup.genes + .filter((g) => validationGeneSet.has(g)) + .slice(0, 20); + + const overlap = mdvTop20.filter((g) => refTop20.includes(g)).length; + const overlapRate = overlap / 20; + + expect(overlapRate).toBeGreaterThanOrEqual(0.6); + }); + + test("significant genes (p < 0.05) overlap with Scanpy", () => { + const mdvSignificant = new Set( + mdvResults.filter((r) => r.pval < 0.05).map((r) => r.gene), + ); + + const validationGeneSet = new Set(validationData.genes); + const refSignificant = new Set( + refGroup.genes.filter( + (g, i) => refGroup.pvals[i] < 0.05 && validationGeneSet.has(g), + ), + ); + + if (refSignificant.size === 0) return; + + // How many of Scanpy's significant genes did we also call significant? + let recalled = 0; + for (const gene of refSignificant) { + if (mdvSignificant.has(gene)) recalled++; + } + const recall = recalled / refSignificant.size; + expect(recall).toBeGreaterThanOrEqual(0.7); + }); + }); + } +}); + +describe("PBMC3k: per-gene sufficient statistics match", () => { + test("Welford mean matches direct computation for a real gene", () => { + const gene = validationData.genes[0]; + const values = validationData.expression[gene]; + const cluster0Indices: number[] = []; + + for (let i = 0; i < validationData.leiden.length; i++) { + if (validationData.leiden[i] === "0") { + cluster0Indices.push(i); + } + } + + // Direct mean + let sum = 0; + for (const idx of cluster0Indices) sum += values[idx]; + const directMean = sum / cluster0Indices.length; + + // Welford mean + const acc = welfordCreate(); + for (const idx of cluster0Indices) welfordAccumulate(acc, values[idx]); + const stats = welfordFinalize(acc); + + expect(stats.mean).toBeCloseTo(directMean, 6); + expect(stats.n).toBe(cluster0Indices.length); + }); + + test("log2FC computed from group means matches Scanpy for a known gene", () => { + const gene = "SNHG12"; // Known DE gene from fixtures + if (!validationData.expression[gene]) return; + + const values = validationData.expression[gene]; + const leiden = validationData.leiden; + + // Compute group means + const accTarget = welfordCreate(); + const accRef = welfordCreate(); + for (let i = 0; i < values.length; i++) { + if (leiden[i] === "0") { + welfordAccumulate(accTarget, values[i]); + } else { + welfordAccumulate(accRef, values[i]); + } + } + const statsTarget = welfordFinalize(accTarget); + const statsRef = welfordFinalize(accRef); + + const mdvLog2FC = log2FoldChange(statsTarget.mean, statsRef.mean); + + // Find Scanpy's log2FC for this gene in cluster 0 + const refGroup = referenceData.groups["0"]; + const refIdx = refGroup.genes.indexOf(gene); + expect(refIdx).toBeGreaterThanOrEqual(0); + const scanpyLog2FC = refGroup.log2fc[refIdx]; + + expect(mdvLog2FC).toBeCloseTo(scanpyLog2FC, 1); + }); + + test("t-test p-value has correct order of magnitude for a known DE gene", () => { + const gene = "SNHG12"; + if (!validationData.expression[gene]) return; + + const values = validationData.expression[gene]; + const leiden = validationData.leiden; + + const accTarget = welfordCreate(); + const accRef = welfordCreate(); + for (let i = 0; i < values.length; i++) { + if (leiden[i] === "0") { + welfordAccumulate(accTarget, values[i]); + } else { + welfordAccumulate(accRef, values[i]); + } + } + const ttest = welchTTest(welfordFinalize(accTarget), welfordFinalize(accRef)); + + const refGroup = referenceData.groups["0"]; + const refIdx = refGroup.genes.indexOf(gene); + const scanpyPval = refGroup.pvals[refIdx]; + + // Both should be significant + expect(ttest.pValue).toBeLessThan(0.01); + expect(scanpyPval).toBeLessThan(0.01); + + // P-values should be in a similar range (within a few orders of magnitude) + if (scanpyPval > 1e-300 && ttest.pValue > 1e-300) { + const log10Ratio = Math.abs( + Math.log10(ttest.pValue) - Math.log10(scanpyPval), + ); + expect(log10Ratio).toBeLessThan(5); + } + }); +}); diff --git a/src/tests/dge-stats.spec.ts b/src/tests/dge-stats.spec.ts new file mode 100644 index 000000000..cb1ddcd31 --- /dev/null +++ b/src/tests/dge-stats.spec.ts @@ -0,0 +1,364 @@ +import { describe, expect, test } from "vitest"; +import { + welfordCreate, + welfordAccumulate, + welfordFinalize, + welchTTest, + log2FoldChange, + clampEffectSize, + benjaminiHochberg, + tDistPValue, + computeGeneStats, + detectDataType, + type GroupStats, +} from "@/datastore/dgeStats"; + +// ── Welford's Online Algorithm ────────────────────────────────────────────── + +describe("Welford online mean/variance", () => { + test("computes correct mean and variance for a known sequence", () => { + const values = [2.3, 3.1, 2.8, 3.5, 2.9, 3.2, 2.7, 3.0, 2.6, 3.3]; + const acc = welfordCreate(); + for (const v of values) welfordAccumulate(acc, v); + const stats = welfordFinalize(acc); + + expect(stats.n).toBe(10); + expect(stats.mean).toBeCloseTo(2.94, 10); + expect(stats.variance).toBeCloseTo(0.12711111111111112, 10); + }); + + test("handles a single value (variance = 0)", () => { + const acc = welfordCreate(); + welfordAccumulate(acc, 42.0); + const stats = welfordFinalize(acc); + + expect(stats.n).toBe(1); + expect(stats.mean).toBe(42.0); + expect(stats.variance).toBe(0); + }); + + test("handles all identical values", () => { + const acc = welfordCreate(); + for (let i = 0; i < 100; i++) welfordAccumulate(acc, 7.0); + const stats = welfordFinalize(acc); + + expect(stats.n).toBe(100); + expect(stats.mean).toBe(7.0); + expect(stats.variance).toBeCloseTo(0, 12); + }); + + test("handles empty accumulator", () => { + const stats = welfordFinalize(welfordCreate()); + expect(stats.n).toBe(0); + expect(stats.mean).toBe(0); + expect(stats.variance).toBe(0); + }); + + test("handles negative values", () => { + const acc = welfordCreate(); + for (const v of [-3, -1, 0, 1, 3]) welfordAccumulate(acc, v); + const stats = welfordFinalize(acc); + + expect(stats.mean).toBeCloseTo(0, 10); + expect(stats.variance).toBeCloseTo(5, 10); + }); + + test("handles large values without catastrophic cancellation", () => { + const acc = welfordCreate(); + const base = 1e8; + const values = [base + 1, base + 2, base + 3, base + 4, base + 5]; + for (const v of values) welfordAccumulate(acc, v); + const stats = welfordFinalize(acc); + + expect(stats.mean).toBeCloseTo(base + 3, 5); + expect(stats.variance).toBeCloseTo(2.5, 5); + }); +}); + +// ── t-Distribution CDF ───────────────────────────────────────────────────── + +describe("tDistPValue", () => { + test("known reference values from scipy.stats.t", () => { + const cases: [number, number, number][] = [ + // [t, df, expected_two_sided_pvalue] + [2.0, 10, 0.07338803477074039], + [3.0, 5, 0.03009924789746257], + [1.96, 100, 0.052778901366229654], + [0.5, 30, 0.6207230048851273], + [10.0, 3, 0.0021283990584141494], + ]; + + for (const [t, df, expected] of cases) { + const result = tDistPValue(t, df); + expect(result).toBeCloseTo(expected, 5); + } + }); + + test("symmetric for positive and negative t", () => { + expect(tDistPValue(2.5, 15)).toBeCloseTo(tDistPValue(-2.5, 15), 10); + }); + + test("t=0 gives p=1", () => { + expect(tDistPValue(0, 10)).toBeCloseTo(1, 10); + }); + + test("very large t gives p near 0", () => { + const p = tDistPValue(50, 100); + expect(p).toBeLessThan(1e-10); + }); + + test("returns NaN for invalid inputs", () => { + expect(tDistPValue(NaN, 10)).toBeNaN(); + expect(tDistPValue(2, NaN)).toBeNaN(); + expect(tDistPValue(2, -1)).toBeNaN(); + }); +}); + +// ── Welch's t-test ────────────────────────────────────────────────────────── + +describe("welchTTest", () => { + test("matches scipy reference for equal-size groups", () => { + const g1: GroupStats = { mean: 2.94, variance: 0.12711111111111112, n: 10 }; + const g2: GroupStats = { mean: 1.45, variance: 0.09166666666666665, n: 10 }; + const result = welchTTest(g1, g2); + + expect(result.tStatistic).toBeCloseTo(10.073599143070807, 4); + expect(result.pValue).toBeCloseTo(1.0273445355608373e-8, 3); + }); + + test("matches scipy reference for unequal-size groups", () => { + const g1: GroupStats = { mean: 7.0, variance: 2.5, n: 5 }; + const g2: GroupStats = { mean: 4.5, variance: 6.0, n: 8 }; + const result = welchTTest(g1, g2); + + expect(result.tStatistic).toBeCloseTo(2.23606797749979, 4); + expect(result.pValue).toBeCloseTo(0.047155428922714536, 4); + }); + + test("returns NaN for groups with < 2 observations", () => { + const g1: GroupStats = { mean: 1.0, variance: 0, n: 1 }; + const g2: GroupStats = { mean: 2.0, variance: 0.5, n: 10 }; + const result = welchTTest(g1, g2); + + expect(result.pValue).toBeNaN(); + }); + + test("returns p=1 for identical groups (zero variance)", () => { + const g1: GroupStats = { mean: 5.0, variance: 0, n: 100 }; + const g2: GroupStats = { mean: 5.0, variance: 0, n: 100 }; + const result = welchTTest(g1, g2); + + expect(result.tStatistic).toBe(0); + expect(result.pValue).toBe(1); + }); + + test("nearly identical groups give high p-value", () => { + const g1: GroupStats = { mean: 1.003, variance: 2.5e-6, n: 5 }; + const g2: GroupStats = { mean: 1.002, variance: 2.5e-6, n: 5 }; + const result = welchTTest(g1, g2); + + expect(result.pValue).toBeCloseTo(0.3465935070873849, 1); + }); +}); + +// ── Log2 Fold Change ──────────────────────────────────────────────────────── + +describe("log2FoldChange", () => { + test("matches Scanpy-style formula: log2((expm1(mean_g) + PSEUDOCOUNT) / (expm1(mean_r) + PSEUDOCOUNT))", () => { + // With PSEUDOCOUNT = 1e-6 + const cases: [number, number, boolean, number][] = [ + [2.0, 1.0, true, 1.894636], // log1p data + [2.0, 1.0, false, 1.0], // linear data: log2((2+1e-6)/(1+1e-6)) approx 1.0 + [0.0, 0.0, true, 0.0], + [0.1, 0.1, true, 0.0], + ]; + + for (const [meanTarget, meanRef, isLog1p, expected] of cases) { + expect(log2FoldChange(meanTarget, meanRef, isLog1p)).toBeCloseTo(expected, 5); + } + }); + + test("positive when target > reference", () => { + expect(log2FoldChange(3.0, 1.0, true)).toBeGreaterThan(0); + }); + + test("negative when target < reference", () => { + expect(log2FoldChange(1.0, 3.0, true)).toBeLessThan(0); + }); + + test("handles zero means (both zero gives 0)", () => { + expect(log2FoldChange(0, 0, true)).toBeCloseTo(0, 10); + }); +}); + +// ── clampEffectSize ───────────────────────────────────────────────────────── + +describe("clampEffectSize", () => { + test("clamps extreme positive values to 10", () => { + expect(clampEffectSize(23.5)).toBe(10); + }); + + test("clamps extreme negative values to -10", () => { + expect(clampEffectSize(-25.0)).toBe(-10); + }); + + test("passes through moderate values unchanged", () => { + expect(clampEffectSize(3.5)).toBe(3.5); + expect(clampEffectSize(-2.1)).toBe(-2.1); + expect(clampEffectSize(0)).toBe(0); + }); + + test("boundary values", () => { + expect(clampEffectSize(10)).toBe(10); + expect(clampEffectSize(-10)).toBe(-10); + }); + + test("handles NaN", () => { + expect(clampEffectSize(NaN)).toBeNaN(); + }); +}); + +// ── detectDataType ────────────────────────────────────────────────────────── + +describe("detectDataType", () => { + test("detects log1p (all non-negative, max <= 20)", () => { + const values = new Float32Array([0, 1.5, 2.3, NaN, 5.0]); + expect(detectDataType(values)).toBe("log1p"); + }); + + test("detects linear (all non-negative, max > 20)", () => { + const values = new Float32Array([0, 10, 50, NaN, 100]); + expect(detectDataType(values)).toBe("linear"); + }); + + test("detects zscored (contains negatives)", () => { + const values = new Float32Array([0, 1.5, -0.5, NaN, 2.0]); + expect(detectDataType(values)).toBe("zscored"); + }); + + test("returns null for all-NaN", () => { + const values = new Float32Array([NaN, NaN, NaN]); + expect(detectDataType(values)).toBeNull(); + }); +}); + +// ── Benjamini-Hochberg Correction ─────────────────────────────────────────── + +describe("benjaminiHochberg", () => { + test("matches statsmodels reference for ordered p-values", () => { + const pvals = [0.001, 0.01, 0.03, 0.05, 0.1, 0.5, 0.8]; + const expected = [0.007, 0.035, 0.07, 0.0875, 0.14, 0.583333333, 0.8]; + const result = benjaminiHochberg(pvals); + + for (let i = 0; i < pvals.length; i++) { + expect(result[i]).toBeCloseTo(expected[i], 6); + } + }); + + test("preserves original index ordering", () => { + const pvals = [0.5, 0.001, 0.1, 0.01]; + const result = benjaminiHochberg(pvals); + + // Smallest p-value at index 1 should have smallest adjusted p-value + expect(result[1]).toBeLessThan(result[3]); + expect(result[3]).toBeLessThan(result[2]); + expect(result[2]).toBeLessThan(result[0]); + }); + + test("all p-values of 1 remain 1", () => { + const result = benjaminiHochberg([1, 1, 1]); + expect(result).toEqual([1, 1, 1]); + }); + + test("single p-value is unchanged", () => { + expect(benjaminiHochberg([0.05])).toEqual([0.05]); + }); + + test("empty array returns empty", () => { + expect(benjaminiHochberg([])).toEqual([]); + }); + + test("adjusted p-values never exceed 1", () => { + const pvals = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]; + const result = benjaminiHochberg(pvals); + for (const p of result) { + expect(p).toBeLessThanOrEqual(1); + } + }); + + test("adjusted p-values are monotonically non-decreasing when input is sorted", () => { + const pvals = [0.001, 0.005, 0.01, 0.02, 0.05]; + const result = benjaminiHochberg(pvals); + for (let i = 1; i < result.length; i++) { + expect(result[i]).toBeGreaterThanOrEqual(result[i - 1]); + } + }); +}); + +// ── computeGeneStats (end-to-end single gene) ────────────────────────────── + +describe("computeGeneStats", () => { + function makeArrays( + values: number[], + groups: number[], + filter?: number[], + ) { + const v = new Float32Array(values); + const g = new Uint8Array(groups); + const f = filter ? new Uint8Array(filter) : new Uint8Array(values.length); + return { v, g, f }; + } + + test("computes correct stats for two clear groups", () => { + // Group 0: [2, 3, 4, 5, 6] mean=4, Group 1: [10, 11, 12, 13, 14] mean=12 + const values = [2, 3, 4, 5, 6, 10, 11, 12, 13, 14]; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]; + const { v, g, f } = makeArrays(values, groups); + + const result = computeGeneStats("TestGene", v, g, f, 0, -1, "linear"); + + expect(result.gene).toBe("TestGene"); + expect(result.meanTarget).toBeCloseTo(4, 5); + expect(result.meanReference).toBeCloseTo(12, 5); + expect(result.effectSize).toBeLessThan(0); // target < reference + expect(result.pval).toBeLessThan(0.001); + }); + + test("respects filterArray (skips filtered cells)", () => { + const values = [100, 2, 3, 4, 5, 10, 11, 12, 13, 14]; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]; + // Filter out the outlier at index 0 + const filter = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]; + const { v, g, f } = makeArrays(values, groups, filter); + + const result = computeGeneStats("TestGene", v, g, f, 0, -1); + + // Without filter: mean would be much higher + // With filter: group 0 is [2,3,4,5] mean=3.5 + expect(result.meanTarget).toBeCloseTo(3.5, 4); + }); + + test("handles specific reference group (not rest)", () => { + const values = [1, 2, 3, 10, 11, 12, 50, 51, 52]; + const groups = [0, 0, 0, 1, 1, 1, 2, 2, 2]; + const { v, g, f } = makeArrays(values, groups); + + // Target=0, Reference=1 (ignore group 2) + const result = computeGeneStats("TestGene", v, g, f, 0, 1); + + expect(result.meanTarget).toBeCloseTo(2, 4); + expect(result.meanReference).toBeCloseTo(11, 4); + }); + + test("treats NaN values as zero (sparse data convention)", () => { + const values = [1, NaN, 3, 10, NaN, 12]; + const groups = [0, 0, 0, 1, 1, 1]; + const { v, g, f } = makeArrays(values, groups); + + const result = computeGeneStats("TestGene", v, g, f, 0, -1); + + // NaN treated as 0: group 0 = [1, 0, 3] mean=4/3, group 1 = [10, 0, 12] mean=22/3 + expect(result.meanTarget).toBeCloseTo(4 / 3, 4); + expect(result.meanReference).toBeCloseTo(22 / 3, 4); + }); +}); diff --git a/src/tests/dge-worker.spec.ts b/src/tests/dge-worker.spec.ts new file mode 100644 index 000000000..edb2c9205 --- /dev/null +++ b/src/tests/dge-worker.spec.ts @@ -0,0 +1,292 @@ +import { describe, expect, test } from "vitest"; +import { processBatch, type DGEBatchInput } from "@/datastore/dgeWorker"; +import { benjaminiHochberg } from "@/datastore/dgeStats"; + +/** + * Tests for the DGE worker's batch processing logic. + * Since vitest runs in jsdom (no real Web Workers), we test + * processBatch() directly -- it contains all the computation. + */ + +function createSharedBuffer( + ArrayType: T, + values: number[], +): SharedArrayBuffer { + const buf = new SharedArrayBuffer(values.length * ArrayType.BYTES_PER_ELEMENT); + const arr = new (ArrayType as any)(buf); + arr.set(values); + return buf; +} + +function makeTestData(opts: { + nCells: number; + nGroups: number; + genes: Record; + filter?: number[]; +}) { + const { nCells, nGroups, genes, filter } = opts; + + const groupAssignments: number[] = []; + const groupSize = Math.floor(nCells / nGroups); + for (let i = 0; i < nCells; i++) { + groupAssignments.push(Math.min(Math.floor(i / groupSize), nGroups - 1)); + } + + const filterArray = filter ?? new Array(nCells).fill(0); + + return { + filterBuffer: createSharedBuffer(Uint8Array, filterArray), + groupBuffer: createSharedBuffer(Uint8Array, groupAssignments), + geneBuffers: Object.values(genes).map((v) => + createSharedBuffer(Float32Array, v), + ), + geneNames: Object.keys(genes), + groupAssignments, + }; +} + +describe("processBatch", () => { + test("returns correct results for clearly different groups", () => { + const nCells = 10; + // Group 0 low with some variance, group 1 high with some variance + const gene1 = [0.1, 0.3, 0.2, 0.5, 0.4, 4.8, 5.2, 5.0, 4.9, 5.1]; + const gene2 = [1.0, 1.1, 0.9, 1.0, 1.0, 1.0, 0.9, 1.1, 1.0, 1.0]; // minimal difference + + const { filterBuffer, groupBuffer, geneBuffers, geneNames } = makeTestData({ + nCells, + nGroups: 2, + genes: { GeneA: gene1, GeneB: gene2 }, + }); + + const result = processBatch({ + type: "run_batch", + filterBuffer, + groupBuffer, + targetGroup: 0, + referenceGroup: -1, + geneBuffers, + geneNames, + batchIndex: 0, + dataIsLog1p: true, + }); + + expect(result.type).toBe("batch_result"); + expect(result.batchIndex).toBe(0); + expect(result.results).toHaveLength(2); + + const geneA = result.results[0]; + expect(geneA.gene).toBe("GeneA"); + expect(geneA.meanTarget).toBeCloseTo(0.3, 1); + expect(geneA.meanReference).toBeCloseTo(5.0, 1); + expect(geneA.effectSize).toBeLessThan(0); + expect(geneA.pval).toBeLessThan(0.001); + + const geneB = result.results[1]; + expect(geneB.gene).toBe("GeneB"); + expect(geneB.pval).toBeGreaterThan(0.05); + }); + + test("correctly handles target vs specific reference group (not rest)", () => { + const nCells = 15; + // 3 groups of 5: group 0 low, group 1 medium, group 2 high + const expression = [1, 1, 1, 1, 1, 5, 5, 5, 5, 5, 10, 10, 10, 10, 10]; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2]; + + const filterBuffer = createSharedBuffer(Uint8Array, new Array(nCells).fill(0)); + const groupBuffer = createSharedBuffer(Uint8Array, groups); + const geneBuffers = [createSharedBuffer(Float32Array, expression)]; + + const result = processBatch({ + type: "run_batch", + filterBuffer, + groupBuffer, + targetGroup: 0, + referenceGroup: 2, + geneBuffers, + geneNames: ["TestGene"], + batchIndex: 0, + dataIsLog1p: true, + }); + + const gene = result.results[0]; + expect(gene.meanTarget).toBeCloseTo(1, 4); + expect(gene.meanReference).toBeCloseTo(10, 4); + // group 1 should be excluded from the reference + }); + + test("respects filter buffer", () => { + const nCells = 10; + // First cell has outlier value 100, but we filter it out + const expression = [100, 2, 3, 4, 5, 10, 11, 12, 13, 14]; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]; + const filter = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0]; // filter out index 0 + + const filterBuffer = createSharedBuffer(Uint8Array, filter); + const groupBuffer = createSharedBuffer(Uint8Array, groups); + const geneBuffers = [createSharedBuffer(Float32Array, expression)]; + + const result = processBatch({ + type: "run_batch", + filterBuffer, + groupBuffer, + targetGroup: 0, + referenceGroup: -1, + geneBuffers, + geneNames: ["Gene"], + batchIndex: 0, + dataIsLog1p: true, + }); + + // With outlier filtered: group 0 = [2,3,4,5], mean = 3.5 + expect(result.results[0].meanTarget).toBeCloseTo(3.5, 4); + }); + + test("handles multiple batches correctly", () => { + const nCells = 20; + const groups: number[] = []; + for (let i = 0; i < nCells; i++) groups.push(i < 10 ? 0 : 1); + + const filterBuffer = createSharedBuffer(Uint8Array, new Array(nCells).fill(0)); + const groupBuffer = createSharedBuffer(Uint8Array, groups); + + // Batch 0: strongly differentially expressed gene + const gene1Expr: number[] = []; + for (let i = 0; i < 10; i++) gene1Expr.push(0.5 + Math.sin(i) * 0.2); + for (let i = 0; i < 10; i++) gene1Expr.push(5.0 + Math.sin(i) * 0.2); + + const result0 = processBatch({ + type: "run_batch", + filterBuffer, + groupBuffer, + targetGroup: 0, + referenceGroup: -1, + geneBuffers: [createSharedBuffer(Float32Array, gene1Expr)], + geneNames: ["Gene1"], + batchIndex: 0, + dataIsLog1p: true, + }); + + // Batch 1: non-differentially expressed gene + const gene2Expr = new Array(nCells).fill(0).map((_, i) => 3.0 + Math.sin(i) * 0.1); + const result1 = processBatch({ + type: "run_batch", + filterBuffer, + groupBuffer, + targetGroup: 0, + referenceGroup: -1, + geneBuffers: [createSharedBuffer(Float32Array, gene2Expr)], + geneNames: ["Gene2"], + batchIndex: 1, + dataIsLog1p: true, + }); + + expect(result0.batchIndex).toBe(0); + expect(result1.batchIndex).toBe(1); + + // Combine and apply BH + const allResults = [...result0.results, ...result1.results]; + const pvals = allResults.map((r) => r.pval); + const padj = benjaminiHochberg(pvals); + + expect(padj).toHaveLength(2); + expect(padj[0]).toBeLessThan(padj[1]); + }); + + test("handles gene with all-zero expression", () => { + const nCells = 10; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]; + const filterBuffer = createSharedBuffer(Uint8Array, new Array(nCells).fill(0)); + const groupBuffer = createSharedBuffer(Uint8Array, groups); + + const result = processBatch({ + type: "run_batch", + filterBuffer, + groupBuffer, + targetGroup: 0, + referenceGroup: -1, + geneBuffers: [createSharedBuffer(Float32Array, new Array(nCells).fill(0))], + geneNames: ["ZeroGene"], + batchIndex: 0, + dataIsLog1p: true, + }); + + const gene = result.results[0]; + expect(gene.meanTarget).toBe(0); + expect(gene.meanReference).toBe(0); + expect(gene.effectSize).toBeCloseTo(0, 6); + // p-value should be 1 (no difference) or NaN (zero variance) + expect(gene.pval === 1 || Number.isNaN(gene.pval)).toBe(true); + }); + + test("handles many genes in a single batch", () => { + const nCells = 20; + const groups: number[] = []; + for (let i = 0; i < nCells; i++) groups.push(i < 10 ? 0 : 1); + + const filterBuffer = createSharedBuffer(Uint8Array, new Array(nCells).fill(0)); + const groupBuffer = createSharedBuffer(Uint8Array, groups); + + const nGenes = 100; + const geneBuffers: SharedArrayBuffer[] = []; + const geneNames: string[] = []; + for (let g = 0; g < nGenes; g++) { + const expr: number[] = []; + for (let i = 0; i < nCells; i++) { + const noise = Math.sin(i * 13 + g * 7) * 0.3; + const groupEffect = i < 10 ? 0 : g * 0.1; + expr.push(2.0 + noise + groupEffect); + } + geneBuffers.push(createSharedBuffer(Float32Array, expr)); + geneNames.push(`Gene${g}`); + } + + const result = processBatch({ + type: "run_batch", + filterBuffer, + groupBuffer, + targetGroup: 0, + referenceGroup: -1, + geneBuffers, + geneNames, + batchIndex: 0, + dataIsLog1p: true, + }); + + expect(result.results).toHaveLength(nGenes); + + // Genes with higher indices (larger effects) should rank near the top + const sortedByPval = [...result.results] + .filter((r) => !Number.isNaN(r.pval)) + .sort((a, b) => a.pval - b.pval); + const topGeneIndex = Number.parseInt(sortedByPval[0].gene.replace("Gene", "")); + expect(topGeneIndex).toBeGreaterThan(80); + }); + + test("handles NaN values in expression", () => { + const nCells = 10; + const groups = [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]; + const expression = [1, NaN, 3, 4, 5, 10, NaN, 12, 13, 14]; + + const filterBuffer = createSharedBuffer(Uint8Array, new Array(nCells).fill(0)); + const groupBuffer = createSharedBuffer(Uint8Array, groups); + const geneBuffers = [createSharedBuffer(Float32Array, expression)]; + + const result = processBatch({ + type: "run_batch", + filterBuffer, + groupBuffer, + targetGroup: 0, + referenceGroup: -1, + geneBuffers, + geneNames: ["NaNGene"], + batchIndex: 0, + dataIsLog1p: true, + }); + + const gene = result.results[0]; + // NaN treated as 0: Group 0 = [1, 0, 3, 4, 5] mean=2.6, Group 1 = [10, 0, 12, 13, 14] mean=9.8 + expect(gene.meanTarget).toBeCloseTo(2.6, 3); + expect(gene.meanReference).toBeCloseTo(9.8, 3); + expect(gene.pval).toBeLessThan(0.05); + }); +}); diff --git a/src/tests/fixtures/dge_reference_ttest.json b/src/tests/fixtures/dge_reference_ttest.json new file mode 100644 index 000000000..97253e255 --- /dev/null +++ b/src/tests/fixtures/dge_reference_ttest.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/src/tests/fixtures/dge_reference_ttest_small.json b/src/tests/fixtures/dge_reference_ttest_small.json new file mode 100644 index 000000000..0eb3d7675 --- /dev/null +++ b/src/tests/fixtures/dge_reference_ttest_small.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/src/tests/fixtures/dge_reference_wilcoxon.json b/src/tests/fixtures/dge_reference_wilcoxon.json new file mode 100644 index 000000000..b08930b5a --- /dev/null +++ b/src/tests/fixtures/dge_reference_wilcoxon.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/src/tests/fixtures/dge_reference_wilcoxon_small.json b/src/tests/fixtures/dge_reference_wilcoxon_small.json new file mode 100644 index 000000000..6d6ef6259 --- /dev/null +++ b/src/tests/fixtures/dge_reference_wilcoxon_small.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/vite.config.mts b/vite.config.mts index ba2b94ffb..86752482f 100644 --- a/vite.config.mts +++ b/vite.config.mts @@ -101,7 +101,8 @@ function getRollupOptions(): RollupOptions { } // avoiding some repition by defining a proxyOptions object used for all proxied routes. -const proxyOptions = { target: flaskURL, changeOrigin: true }; +// NOTE: Large chunked responses can be sensitive to proxy timeouts. +const proxyOptions = { target: flaskURL, changeOrigin: true, timeout: 0, proxyTimeout: 0 }; // ... and then this is a bit more concise than const proxy = [ '^/(get_|images|tracks|save|chat).*', // these routes are proxied to flask server in 'single project' mode @@ -172,9 +173,9 @@ export default defineConfig(env => { strictPort: true, proxy, }, - publicDir: process.env.exclude_dir?false:'examples', //used for netlify.toml??... the rest is noise. + publicDir: process.env.exclude_dir ? false : "examples", //used for netlify.toml??... the rest is noise. build: { - sourcemap: process.env.nomap?false:true, + sourcemap: !process.env.nomap, rollupOptions: { ...getRollupOptions(), external: ['./python/**'],